Cortex-Like Learning Machine for Temporal and Hierarchical Pattern Recognition

ABSTRACT

A cortex-like learning machine, called a probabilistic associative memory (PAM), is disclosed for recognizing spatial and temporal patterns. A PAM is usually a multilayer or recurrent network of processing units (PUs). Each PU expands subvectors of a feature subvector input to the PU into orthogonal vectors, and generates a probability distribution of the label of said feature subvector, using expansion correlation matrices, which are adjusted in supervised or unsupervised learning by a Hebb rule. The PU also converts the probability distribution into a ternary vector to be included in feature subvectors that are input to PUs in the same or other layers. A masking matrix in each PU eliminates effect of corrupted components in query feature subvectors and enables maximal generalization by said PU and thereby that by the PAM. PAMs with proper learning can recognize rotated, translated and scaled patterns and are functional models of the cortex.

1 CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of provisional patent application Ser. No. 61/128,499, filed 2008 May 22 by the present inventor.

2 BACKGROUND AND ADVANTAGES OF THE INVENTION

In the terminology of pattern recognition, neural networks and machines learning, a feature vector is a transformation of a measurement vector, whose components are measurements or sensor outputs. This invention is mainly concerned with processing feature vectors and sequences of related feature vectors for detecting and recognizing spatial and temporal causes (e.g., objects in images/video, words in speech, and characters in handwriting). This is what pattern recognition, neural networks and machines learning are essentially about. It is also a typical problem in the fields of computer vision, signal processing, system control, telecommunication, and data mining. Example applications that can be formulated as such a problem are handwritten character classification, face recognition, fingerprint identification, DNA sequence identification, speech recognition, machine fault detection, baggage/container examination, video monitoring, text/speech understanding, automatic target recognition, medical diagnosis, prosthesis control, robotic arm control, and vehicle navigation.

A good introduction to the prior art in pattern classification, neural networks and machine learning can be found in Simon Haykin, Neural Networks and Learning Machines, Third Edition, Pearson Education, New Jersey, 2009; Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer Science, New York, 2006; Neural Networks for Pattern Recognition, Oxford University Press, New York, 1995; B. D. Ripley, Pattern Recognition and Neural Networks, Cambridge University Press, New York, 1996; S. Theodoridis and K. Koutroumbas, Pattern Recognition, Second Edition, Academic Press, New York, 2003; Anil K. Jain, Robert P. W. Duin and Jianchang Mao, “Statistical Pattern Recognition: A Review,” in IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, January 2000; R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, second edition, John Wiley & Sons, New York, 2001; and Bernhard Scholkopf and Alexander J. Smota, Learning with Kernels, The MIT Press, Cambridge, Mass., 2002.

Commonly used pattern classifiers include template matching, nearest mean classifiers, subspace methods, 1-nearest neighbor rule, k-nearest neighbor rule, Bayes plug-in, logistic classifiers, Parzen classifiers, Fisher linear discriminants, binary decision trees, multilayer perceptrons, radial basis networks, and support vector machines. They each are suitable for some classification problems. However, in general, they all suffer from some of such shortcomings as difficult training/design, much computation/memory requirement, ad hoc character of the penalty function, or poor generalization/performance. For example, the relatively more powerful multilayer perceptrons and support vector machines are difficult to train, especially if the dimensionality of the feature vectors is large. After training, if new training data is to be learned, the trained multilayer perceptron or support vector machine is usually discarded and new one is trained over again. Its decision boundaries are determined by exemplary patterns from all classes. Furthermore, if there are a great many classes or if there are no or not enough exemplary patterns for some “confuser classes” such as for target and face recognition, training an MLP or SVM either is impractical or incurs a high misclassification rate. Camouflaged targets or occluded faces not included in the training data are known to also cause high misclassification rates.

A pattern classification approach, that is relatively seldom mentioned in the pattern recognition literature, is the correlation matrix memories, or CMMs, which have been studied essentially in the neural networks community (T. Kohonen, Self-Organization and Associative Memory, second edition, Springer-Verlag, 1988; R. Hecht-Nielsen, Neurocomputing, Addison-Wesley, 1990; Branko Soucek and The Iris Group, Fuzzy, Holographic, and Parallel Intelligence—The Sixth-Generation Breakthrough, edited, John Wiley and Sons, 1992; James A. Anderson, An Introduction to Neural Networks, The MIT Press, 1995; S. Y. Kung, Digital Neural Networks, Pearson Education POD, 1997; D. J. Willshaw, O. P. Buneman and H. C. Longet-Higgins, “Non-holographic associative memory,” Nature, 222, pp. 960-962, 1969; D. J. Willshaw and H. C. Longet-Higgins, “Associative memory models,” Machine Intelligence, vol. 5, edited by B. Meltzer & O. Michie, Edinburgh University Press, 1970; K. Nagano, “Association—a model of associative memory,” IEEE Transactions on Systems, Man and Cybernetics, vol. SMC-2, pp. 68-70, 1972; G. Palm, “On associative memory,” Biological Cybernetics, vol. 36, pp. 19-31, 1980; E. Gardner, “The space of interactions in neural network models,” Journal of Physics, vol. A21, pp. 257-270, 1988; S. Amari, “Characteristics of sparsely encoded associative memory,” Neural Networks, vol. 2(6), pp. 451-457, 1989; J. Buckingham and D. Willshaw, “On setting unit thresholds in an incompletely connected associative net,” Network, vol. 4, pp. 441-459, 1993; M. Turner and J. Austin, “Matching Performance of Binary Correlation Matrix Memories,” Neural Networks, 1997). The training of CMMs, which are associative memories, is easy and fast even if they have a very high dimensional input. If new training data is to be learned or if the dimensionality of a trained CMM is to be modified, the CMM is not discarded, but can be easily updated or expanded.

Two types of CMM are noteworthy. They are the holographic neural nets (John Sutherland, “Artificial neural device utilizing phase orientation in the complex number domain to encode and decode stimulus response patterns,” U.S. Pat. No. 5,214,745, May 25, 1993; John Sutherland, “Neural networks,” U.S. Pat. No. 5,515,477, May 7, 1996) and the binary CMMs in the aforementioned papers by Willshaw and Longuet-Higgins (1970), Palm (1980), Gardner (1988), S. Amari (1989), M. Turner and J. Austin (1997), and the references therein.

The main idea of holographic neural nets (IINets) is representing real numbers by phase angle orientations on a complex number plane through the use of a sigmoidal transformation such as a hyperbolic tangent function. After each component of the input stimuli and output responses is converted into a complex number whose phase angle orientation (i.e. argument) represents the component, the correlation matrix is constructed in the standard manner. A holographic neural cell comprises essentially such a correlation matrix. If the dimensionality of the stimulus is large enough, augmented if necessary, and if the phase angle orientations of the stimuli and responses are more or less statistically independent and uniformly distributed on the unit circles in the complex number plane, the “signal part” in the response to an input stimulus is hopefully much greater than the “interference part” in the response to the same input stimulus during its retrieval because of self-destruction of those stored stimuli that are out of phase with said input stimulus like the self-destruction of a random walk on the complex number plane. This idea allows more stimuli to be stored in a complex correlation matrix than does the earlier versions of the correlation matrix.

However, the holographic neural cell approach suffers from the following shortcomings. First, to avoid ambiguity at the point, (−1,0)=−1+0i, in the complex plane, a neighborhood of (−1,0) must be excluded in the range of the sigmoidal transformation. This prevents the mentioned uniform distribution required for good self-destruction of the interference part. Second, it is not clear how to augment the stimuli without introducing much correlations among the stimuli, which again may reduce self-destruction of the interference part. Third, the argument of a complex number on the unit circle ranges from −π to π. To pack more stimuli on it, better self-destruction of the interference part is needed, which in turn requires a higher dimensionality of the stimuli. Such a higher dimensionality means a higher dimensionality of the correlation matrix, requiring more memory space to hold the matrix.

Binary CMMs have feature vectors encoded either into unipolar binary vectors with components equal to 1 or 0 or into bipolar binary vectors with components equal to 1 or −1. Bipolar binary vectors were used in most of the earlier work on binary CMMs. Superiority of sparse unipolar binary encoding (with most of the components of encoded feature vectors being 0 and only a few being 1) to nonsparse unipolar binary encoding and bipolar binary encoding was remarked and proved in the mentioned papers by Willshaw and Longuet-Higgins (1970), Palm (1980), Gardner (1988), and S. Amari (1989). Sparsely encoded CMMs are easy to implement (J. Austin and J. Kennedy, “A hardware implementation of a binary neural network,” MicroNeuro, IEEE Computer Press, 1994), and have found many applications. Nevertheless, sparsely encoded CMMs have quite a few shortcomings: (a) A large sparse correlation matrix has very low “information density” and takes much memory space. (b) A multistage sparsely encoded CMMs is often necessary. (c) There is no systematic way to determine the dimensionality of the sparse unipolar binary vectors to represent the feature vectors. (d) There is no systematic way to determine the number of stages or the number of neurons in each stage in a multistage sparsely encoded CMM. (e) There is no systematic way to determine whether a sparsely encoded CMM has a minimum misclassification probability for the given CMM architecture. (f) The mapping from the feature vectors to their representative sparse binary vectors must be stored in some memory space, further reducing the overall memory density of the CMM.

Judging from the foregoing shortcomings of the commonly used pattern classifiers, the holographic neural nets, and the sparsely encoded CMMs, there remains a need for alternatives to existing pattern classifiers in the prior art for recognizing patterns.

In this invention disclosure, a cortex-like learning machine, called a probabilistic associative memory (PAM), is disclosed that processes feature vectors or sequence of feature vectors, each feature vector being a ternary feature vector. In multilayer PAMs without feedback connections, each layer other than the last processes and transforms a feature vector into another feature vector input to the next layer. To distinguish feature vectors input to a PAM from feature vectors generated internally in the PAM, the former are called exogenous feature vectors.

A PAM is a network of processing units (PUs). It can be viewed as a new neural network paradigm or a new type of learning machine. Each PU generates a representative of a subjective probability distribution of the label of a feature subvector or a sequence of feature subvectors that appear in its receptive field. Some PUs convert such representatives into ternary vectors, which are included in feature subvectors input to other PUs. Weights in a PU learn an input feature subvector with or without supervision by a Hebb rule of learning. Some advantages of PAMs are the following:

-   -   1. As opposed to most of commonly used pattern recognizers, a         PAM generalizes not by only a single holistic similarity         criterion for the entire input exogenous feature vector, which         noise, erasure, distortion and occlusion can easily defeat, but         by a large number of similarity criteria for feature subvectors         input to a large number of PUs (processing units) in different         layers. These criteria contribute individually and collectively         to generalization for single and multiple causes. Example 1:         smiling; putting on a hat; growing or shaving beard; or wearing         a wig can upset a single similarity criterion used for         recognizing a face in a mug-shot photograph. However, a face can         be recognized by each of a large number of feature subvectors of         the face. If one of them is recognized to belong to a certain         face, the face is recognized. Example 2: a typical kitchen         contains a refrigerator, a counter top, sinks, faucets, stoves,         fruit and vegetable on a table, etc. The kitchen is still a         kitchen if a couple of items, say the stoves and the table with         fruit and vegetable, are removed.     -   2. Masking matrices in a PU eliminate effects of corrupted         ternary components of the feature subvector input to the PU, and         thereby enable maximal generalization capability of the PU, and         in turn that of the PAM.     -   3. PAMs are neural networks, but are no more blackboxes with         “fully connected” layers much criticized by opponents of such         neural networks as multilayer perceptrons (MLPs) and recurrent         MLPs, whose weights are iteratively determined through         minimizing an error criterion and have no interpretation in the         context of their applications. In a PU of a PAM, weights are         correlations between an orthogonal expansion of its input         feature subvector and its label. Each PU has a receptive region         in the exogenous feature vector input to the PAM and classifies         any cause within the receptive region. Such interpretations can         be used to help select the architecture (i.e., layers, PUs,         connections, feedback structures, etc.) of a PAM for the         application.     -   4. The weights in each PU of a PAM learn by a Hebb rule and thus         the PAM has a “photographic memory.” No iterative optimization         such as that involved in local-search training methods using         backpropagation or backpropagation through time is needed for         learning. This allows easy learning of a large number of large         exogenous feature vectors in reasonable time as well as easy         online adaptive learning.     -   5. A PU can learn with or without supervision. This allows a PAM         to (1) perform unsupervised deep learning in lower layers and         supervised learning in higher layers; (2) perform supervised         learning when a label is provided from outside the PAM and         unsupervised learning when not; and (3) perform autonomous         learning.     -   6. A PAM may have some capability of recognizing rotated,         translated and scaled patterns. Moreover, easy learning and         retrieving by a PAM allow it to learn translated, rotated and         scaled versions of an input image with ease.     -   7. PUs generate representatives of probability distributions of         the labels of their input feature subvectors. Such         representatives of a common label can be combined into a single         representative of probability distributions of the common label.     -   8. PAMs with hierarchical and feedback structures can detect and         recognize multiple and hierarchical causes in a spatial or         temporal exogenous feature vector.     -   9. The weight matrices (e.g., expansion correlation matrices) in         different PUs can be added to combine the learned knowledge at         virtually no additional cost.     -   10. The architecture of a PAM can be adjusted without discarding         learned knowledge in the PAM. This allows enlargement of the         feature subvectors, increase of the number of layers, and even         increase of feedback connections.     -   11. Only a small number of algorithmic steps of parallel         computing are needed for retrieval, which are suitable for         massive parallelization at the bit level and by VLSI         implememtation.

3 SUMMARY

An objective of the present invention is to provide a system that learns to recognize spatial or temporal patterns or causes.

Another objective of the present invention is to provide a system that can learn and recognize partially erased, smeared, noised, covered, distorted, or altered patterns.

Still another objective of the present invention is to provide a system that can perform supervised or unsupervised learning or both.

Still another objective of the present invention is to provide a system whose architecture can be adjusted without discarding learned knowledge.

Still another objective of the present invention is to provide a system that produces representatives of probability distributions of labels of feature subvectors.

Still another objective of the present invention is to provide a system with a hierarchical structure for recognizing hierarchical causes (or patterns) at different levels such as line segments in a letter, ears; nose; mouth and eyes on a face, and apples and oranges in baskets on a table.

Still another objective of the present invention is to provide a system with feedback structures for processing sequences of related exogenous feature vectors such as those obtained from examining one single image for a plurality of consecutive time points, images of an object from different angles, consecutive frames in a video or movie, and handwritten letters in a word, words in a sentence, and sentences in a paragraph.

Still another objective of the present invention is to provide a system that can recognize rotated, translated and/or scaled versions of images that have been learned by the system.

Still another objective of the present invention is to provide a system that learns autonomously.

The foregoing objectives, as well as other objectives of the present invention that will become apparent from the discussion below, are achieved, by the present invention with the following preferred embodiments.

A first major embodiment of the present invention disclosed herein is a system for processing feature vectors. Such feature vectors that are input to the system are also called exogenous feature vectors to distinguish them from feature vectors that are generated internally by said system. The system comprises at least one processing unit (PU) that comprises (1) storage means for storing at least one expansion correlation matrix; (2) expansion means, responsive to a feature subvector, for generating an orthogonal expansion of a subvector of said feature subvector; and (3) estimation means for using said orthogonal expansion and said expansion correlation matrix to produce a representative of a probability distribution of a label of said feature subvector.

A second major embodiment of the present invention is a system for processing exogenous feature vectors. Such feature vectors that are input to said system are also called exogenous feature vectors to distinguish them from feature vectors that are generated internally by said system. The system comprises ordered layers of PUs (processing units). Each of these PUs comprises (1) storage means for storing at least one general expansion correlation matrix; (2) expansion means, responsive to a feature subvector input to said processing unit, for generating a general orthogonal expansion of said feature subvector; and (3) estimation means for using said at least one general expansion correlation matrix and said general orthogonal expansion to produce a representative of a probability distribution of a label of said feature subvector. At least one of said PUs further comprises conversion means for converting said representative of a probability distribution into a vector of ternary numbers, which is a point estimate of said label. Exogenous feature vectors are input to the lowest-ordered layer. Some components of a feature vector that is input to a certain layer other than said lowest-ordered layer are produced by conversion means in PUs in layers ordered lower than said certain layer.

Another embodiment of the present invention is either the first or the second major embodiment, wherein at least one expansion correlation matrix is an expansion correlation matrix on a rotation/translation/scaling (RTS) suite of a subvector of a feature subvector index. Such an expansion correlation matrix helps the embodiment recognize rotated, translated and scaled causes or objects.

Still another embodiment of the present invention is either the first or the second major embodiment, wherein at least one expansion correlation matrix is a sum of expansion correlation matrices on rotation/translation/scaling (RTS) suites of translations of a subvector of a feature subvector. Such an expansion correlation matrix helps the embodiment recognize moderately rotated and scaled and extensively translated causes or objects.

Still another embodiment of the present invention is either the first or the second major embodiment, wherein each processing unit further comprises a masking matrix, responsive to an orthogonal expansion of a subvector of a feature subvector, for setting an automatically selected number of components of said subvector equal to zero to allow said estimation means to use an orthogonal expansion of the resultant subvector and an expansion correlation matrix to produce a representative of a probability distribution of a label of said feature subvector. The purpose of said masking matrix is to eliminate effect of said components and thereby generalize on the remaining components of said subvector.

Still another embodiment of the present invention is either the first or the second major embodiment, wherein a plurality of expansion correlation matrices are submatrices of a general expansion correlation matrix, a plurality of masking matrices are submatrices of a general masking matrix, and a plurality of orthogonal expansions are subvectors of a general orthogonal expansion. The dimensionality of a general orthogonal expansion is usually much smaller than the dimensionality of an orthogonal expansion of the feature subvector input to the PU. A purpose of this embodiment is to ensure the dimensionalities of vectors and matrices involved in the PU manageable. Another purpose of this embodiment is help masking matrices enhance the generalization capability of the PU.

Still another embodiment of the present invention is either the first or the second major embodiment, wherein at least one PU is a supervised PU, an unsupervised PU, or a supervised/unsupervised PU.

Still another embodiment of the present invention is either the first or the second major embodiment, wherein at least one PU further comprises supervised learning means, responsive to a feature subvector and a label provided from outside the embodiment, for adjusting expansion correlation matrices.

The third major embodiment of the present invention is the first major embodiment, wherein at least one PU further comprises conversion means for converting a representative of a probability distribution produced by said estimation means into at least one ternary number, and at least one ternary number generated by said conversion means is included in a feature subvector that is input to another PU.

Still another embodiment of the present invention is either the third embodiment or the second major embodiment, wherein at least one PU further comprises unsupervised learning means, responsive to a feature subvector, for adjusting an expansion correlation matrix by steps of

-   -   1. generating a label of said feature subvector by processing         said feature subvector by said processing unit to produce a         plurality of ternary numbers, and using said ternary numbers as         components of said label; and     -   2. adjusting said expansion correlation matrix using said label         and orthogonal expansions of subvectors of said feature         subvector.

Still another embodiment of the present invention is either the third or second major embodiment for processing exogenous feature vectors in sequences of exogenous feature vectors, wherein a plurality of ternary numbers generated by a first PU in processing a first exogenous feature vector in a sequence are included, after a time delay, in a second feature subvector that is input to a second PU, in processing a second exogenous feature vector subsequent to said first exogenous feature vector in said sequence. If said second PU belongs to a layer not higher ordered than the layer that said first PU belongs to, this embodiment is a recurrent network of PUs with feedback means.

Still another embodiment of the present invention is either the first or the second major embodiment, further comprising combination means for combining a plurality of representatives of probability distributions of a common label of feature subvectors into a representative of a probability distribution of said common label.

Still another embodiment of the present invention is either the first or the second major embodiment, wherein a plurality of ternary vectors are generated by conversion means in response to each exogenous feature vector.

The fourth major embodiment of the present invention is a machine for producing an orthogonal expansion {hacek over (v)} of a ternary vector v=[v₁ v₂ . . . v_(M)]′, where the prime ′ denotes matrix transposition, said machine comprising

-   -   1. multiplication means, responsive to two ternary digits, for         producing a product of said two ternary digits; and     -   2. evaluation means for using said multiplication means for         evaluating and including components of an othogonal expansion         {hacek over (v)} of v.

Another embodiment of the present invention is the the above embodiment, wherein said evaluation means first sets {hacek over (v)}(1) equal to [1 v₁]′, and then uses a recursive formula, {hacek over (v)}(1, . . . , j+1)=[{hacek over (v)}′(1, . . . , j) v_(j+1){hacek over (v)}′(1, . . . , j)]′, to generate {hacek over (v)}(1, . . . , j+1) for j=1, . . . , M−1 recursively, the resulting vector {hacek over (v)}(1, . . . , M) being said orthogonal expansion {hacek over (v)}.

4 DESCRIPTION OF DRAWINGS

Embodiments of the invention disclosed herein, which are called probabilistic associative memories (PAMs), comprise at least one processing unit (PU). Component parts of a PU are first shown in the drawings described below. Drawings are then given to show how these component parts are used to construct some embodiments of the present invention. Embodiments of the present invention that can recognize rotated, translated and scaled causes (e.g., objects) and their component parts are also shown in drawings.

In the present invention disclosure, the prime ′ denotes matrix transposition, and a vector is regarded as a subvector of the vector itself, as usual.

FIG. 1 shows expansion means 1 for generating an orthogonal expansion {hacek over (v)} of a given ternary vector v=[v₁ . . . v_(k)]′ in a recursive manner. Starting with {hacek over (v)}(1):=[1 v₁]′, the recursive formula,

{hacek over (v)}(1, . . . ,j+1)=[{hacek over (v)}′(1, . . . ,j) v _(j−1) {hacek over (v)}′(1, . . . ,j)]′,

evaluates {hacek over (v)}(1, . . . ,j) for j=2, . . . , k−1, yielding {hacek over (v)}={hacek over (v)}(1, . . . , k).

FIG. 2 shows expansion means 2 for generating general orthogonal expansion {hacek over (v)} of a given ternary vector v=[v₁ . . . v_(m)]′. Let n=[n₁ . . . n_(k(n))]′ be a subvector of [1 . . . k]′ whose components, n₁, . . . , n_(k(n)) satisfy 1≦n₁< . . . <n_(k(n))≦k. The subvector n is called a subvector index. The subvector v(n)=[v_(n) ₁ . . . v_(n) _(k(n)) ]′ of v is called a subvector of v on the subvector index n. Given subvector indices, 1, . . . , N, which may or may not have common components, the expansion means 2 shown in this figure (FIG. 2) works as follows: The orthogonal expansions, {hacek over (v)}(1), . . . , {hacek over (v)}(N), of subvectors, v(1), . . . , v(N), are first obtained using the expansion means shown in FIG. 1. Then the orthogonal expansions, {hacek over (v)}(1), . . . , {hacek over (v)}(N), are included as block components in the vector {hacek over (v)}=[{hacek over (v)}′(1) . . . {hacek over (v)}′(N)]′. The resultant vector {hacek over (v)} is called a general orthogonal expansion of v.

A PAM usually has a plurality of ordered layers, and a layer usually has a plurality of PUs (processing units). A vector input to layer l is called a feature vector and denoted by x_(t) ^(l 1)=[x_(t1) ^(l−1) x_(t2) ^(l−1) . . . x_(tM) ^(l−1)]′, t is used to distinguish feature vectors that are input at different times (or with different numberings). A vector that is input to a PU (processing unit) in layer l is a subvector of a feature vector x_(t) ^(l−1). The subvector index of said subvector of the feature vector x_(t) ^(l−1) is called a feature subvector index (FSI). A feature subvector index (FSI) is denoted by a lower-case boldface letter. A symbol to denote a typical FSI is n and the subvector x_(l) ^(l−1) (n) is called the feature subvector on the FSI n.

FIG. 3 shows an example FSI (feature subvector index) n=[2 4 6 8 11 14 16 19 21 23 25 27 30 33 35 38]′, which is identified with pixel locations. An example subvector [2 6 14 19 23 33]′, say the third subvector, of the feature subvector n is indicated by 46. The example subvector is denoted by n(3)=[2 6 14 19 23 33]′.

FIG. 4 shows expansion correlation matrices (ECMs), C(n(u)) and D(n(u)), on the subvector n(u) of a feature vector index n, that are defined by the pairs, (x_(t)(n(u)), r_(t)(n)), t=1, . . . , T, with weight matrices W_(t)(n(u),T), where x_(t)(n(u)) and r_(t)(n) are a subvector on n(u) of a feature subvector x_(t)(n) on n and an R-dimensional label of x_(t)(n), respectively. Note that here r_(t)(n)≠0, for t=1, . . . , T. If a label r_(T)(n)=0, (x_(T)(n(u)), r_(T)(n)) is skipped and not included in the numbering, t=1, . . . , T, or in the pairs, (x_(t)(n(u)), r_(t)(n)), t=1, . . . , T. This is also the case with FIG. 8, FIG. 19 and FIG. 22. A denotes a scaling constant, which is usually a small positive number selected to keep the numbers involved in an application of the present invention manageable. Note that [C′(n(u)) D′(n(u))] can be viewed as a single ECM. W_(t)(n(u),T) is usually selected to be equal to w_(t)(n(u),T) I, for some real-valued function w_(t)(n(u),T), where I is an R×R identify matrix, R being the dimensionality of r_(t)(n). If this is the case, we define

${C\left( {n(u)} \right)} = {\Lambda {\sum\limits_{t = 1}^{T}{{w_{t}\left( {{n(u)},T} \right)}{{\overset{\Cup}{x}}_{t}^{\prime}\left( {n(u)} \right)}}}}$

instead. Here C(n(u)) is a row vector, and the ECM [C′(n(u)) D′(n(u))]′ has R+1 rows.

FIG. 5 shows, if the label r_(T)(n)≠0, how the expansion correlation matrices (ECMs), C(n(u)) and D(n(u)), defined in FIG. 4 with the weight matrix W_(t)(n(u),T)=λ^(T−t)I are adjusted to learn a pair (x_(T)(n(u)), r_(T)(n)). If r_(T)(n)=0, the pair (x_(T)(n(u)), r_(T)(n)) is discarded. λ is a forgetting factor, and Λ is a scaling constant. Note that W_(t)(n(u),T) is a diagonal matrix with equal diagonal entries, C(n(u)) has only one row, and the ECM [C′(n(u)) D′(n(u))]′ has only R+1 rows.

FIG. 6 shows, if the label r_(T)(n(u))≠0, how the expansion correlation matrices (ECMs), C(n(u)) and D(n(u)), defined in FIG. 4 for the weight matrix W_(t)(n(u),T)=(1/√{square root over (T)}) I are adjusted to learn a pair (x_(T)(n(u)), r_(T)(n(u))). The number T of learned pairs is updated to T+1 after a pair is learned. If r_(T)(n)=0, the pair (x_(T)(n(u)), r_(T)(n)) is discarded, and the number is not updated. Note that W_(t)(n(u),T) is a diagonal matrix with equal diagonal entries, C(n(u)) has only one row, and the ECM [C′(n(u)) D′(n(u))]′ has only R+1 rows.

FIG. 7 shows a masking matrix M(n(u)) on a subvector n(u) of an FSI (feature subvector index) n, where I=[1 . . . 1]′, I(i₁ ⁻, i₂ ⁻, . . . , i_(j) ⁻) is the vector I with its i₁-th, i₂-th, . . . , and i_(j)-th components set equal to 0, {hacek over (I)}(i₁ ⁻, i₂ ⁻, . . . , i_(j) ⁻) is the orthogonal expansion of I(i₁ ⁻, i₂ ⁻, . . . , i_(j) ⁻), and diag({hacek over (I)}(i₁ ⁻, i₂ ⁻, . . . , i_(j) ⁻)) denotes the diagonal matrix whose diagonal entries are {hacek over (I)}(i₁ ⁻, i₂ ⁻, . . . , i_(j) ⁻). Λ masking matrix M(n(u)) is used to set automatically selected components of a subvector x_(T)(n(u)) of a feature subvector x_(T)(n) equal to 0 in order to retrieve the label of a feature subvector stored in ECMs that shares the largest number of components with x_(T)(n(u)). Note that 2⁻⁸ is an example weight factor selected to differentiate between different levels of maskings to effect the automatic selection. The weight should be selected to suit the application. Note that as usual, I denotes an identity matrix, and I=diag I.

FIG. 8 shows a general orthogonal expansion {hacek over (x)}_(t)(n) of a feature subvector x_(t)(n) and general expansion correlation matrices, C(n) and D(n), on the FSI n with subvectors n(u), u=1, . . . , U. r_(t)(n) in the definition formulas of C(n) and D(n) are assumed to be not equal to 0. If a label r_(T)(n)=0, (x_(T)(n(u)), r_(T)(n)) is skipped and not included in the numbering, t=1, . . . , T, or in the pairs, (x_(t)(n(u)), r_(t)(n)), t=1, . . . , T. This is also the case with FIG. 4, FIG. 19 and FIG. 22. {hacek over (x)}_(t)(n) has orthogonal expansions {hacek over (x)}_(t)(n(u)), u=1, . . . , U, as its subvectors, and C(n) has expansion matrices C(n(u)), u=1, . . . , U, as its submatrices, and D(n(u)) has expansion matrices D(n(u)), u=1, . . . , U, as its submatrices. It is understood that an orthogonal expansion of x_(t)(n) is a special case of a general orthogonal expansion of x_(t)(n), and an expansion correlation matrix on n is a special case of a general expansion correlation matrix on n.

FIG. 9 shows a general masking matrix M(n) on an FSI n with subvectors n(u), u=1, . . . , U. M(n) has M(n(u)), u=1, . . . , U, as its diagonal blocks. M(n(u)) are defined in FIG. 7. M(n) and M(n(u)) are represented by M for simplicity if the context makes it clear which M represents. It is understood that a masking matrix on n is a special case of a general masking matrix on n.

FIG. 10 shows estimation means 54 for using a general orthogonal expansion {hacek over (x)}_(T)(n) of the input feature subvector x_(T)(n) and general expansion correlation matrices, C(n) and D(n), to produce a representative, y_(T)(n)=2p_(T)(n)−I, of a probability distribution of the label r_(T)(n) of the feature subvector x_(T)(n) on the feature subvector index n. In the figure, a general masking matrix M(n) is represented by M. Note that as a special case, the general expansion correlation matrices and general orthogonal expansion can be simply expansion correlation matrices on n, and an orthogonal expansion of x_(T)(n), respectively. Here R is the dimensionality of the label r_(T)(n), and p_(Tk)(n) is the subjective probability that the k-th component of r_(T)(n) is +1. Note that every component of y_(T)(n)=2p_(T)(n)−I lies between −1 and 1.

FIG. 11 shows an example conversion means 13 a for converting a representative y_(T)(n)=2p_(T)(n)−I of a probability distribution into a ternary vector x{y_(T)(n)}. Every component y_(Tk)(n) of y_(T)(n) is converted into a a ternary number (or a one-dimensional ternary vector) x{y_(Tk)(n)} as follows: Generate a pseudo-random number in accordance with the probability distribution of a random variable v: P(v=1)=p_(Tk)(n) and P(v=−1)=1−p_(Tk)(n), and set x{y_(Tk)(n)} equal to the obtained pseudo-random number.

The output x{y_(T)(n)} that conversion means generates is an R-dimensional vector with components x{y_(Tk)(n)}, k=1, . . . , R. x{y_(T)(n)} is a point estimate of r_(T)(n).

FIG. 12 shows an alternative conversion means 13 b for converting a representative y_(T)(n)=2p_(T)(n)−I of a probability distribution into a ternary vector x{y_(T)(n)}. Assume that each component y_(Tk)(n) of y_(T)(n) is to be converted into a three-dimensional ternary vector. Recall that −1≦y_(Tk)(n)≦1. If y_(Tk)(n) is very close to 0, the probability p_(Tk)(n) is very close to ½ and contains little information about the label r_(Tk)(n). To eliminate it from further processing, the conversion means converts it into x{y_(Tk)(n)}=[0 0 0]. If y_(Tk)(n) is not very close to 0, we convert it into a 3-component ternary vector x{y_(Tk)(n)} as shown in FIG. 12. The output x{y_(T)(n)} of the converter is a 3R-dimensional concatenation of x{y_(Tk)(n)}, k=1, . . . , R. The method of converting a component y_(Tk)(n) of y_(T)(n) into a 3-dimensional ternary vector can easily be generalized to a method of converting y_(Tk)(n) into a ternary vector of any dimensionality.

FIG. 13 shows an example processing unit (PU) on a feature subvector index n (PU(n)). In retrieving, a feature suvector x_(T)(n) on the FSI n is first expanded into a GOE (general orthogonal expansion) {hacek over (x)}_(T)(n) by the expansion means 2. {hacek over (x)}_(T)(n) is then processed by the estimation means 54, using the GECMs (general expansion correlation matrices), C(n) and D(n), from the storage 56, to obtain a representative y_(T)(n) of a probability distribution of the label of x_(T)(n). The conversion means 13 converts y_(T)(n) into a ternary vector x{y_(T)(n)}, which is an output of the PU. If a representative of a probability distribution of x_(T)(n) is needed for use outside the PU, y_(T)(n) is also output by the PU. The dashed line in the arrow 55 indicates “output as needed.” This process of generating y_(T)(n) and x{y_(T)(n)} by PU(n) is called retrival of a label of the feature subvector x_(T)(n) by PU(n).

If a label r_(T)(n)≠0 of x_(T)(n) from outside the PU is available for learning, and learning x_(T)(n) and r_(T)(n) is wanted, supervised learning is performed by the PU. In supervised learning, the class label r_(T)(n)≠0 is received through a lever represented by a thick solid line with a solid dot in the position 48 by an adjustment means 9, which receives also {hacek over (x)}_(T)(n) from the expansion means 2 and uses a method of adjusting ECMs such as those depicted in FIG. 5 and FIG. 6 and assembles the resultant ECMs C(n(u)) and D(n(u)), u=1, . . . , U, into general ECMs

C(n)=[C(n(1)) C(n(2)) C(n(U))]

D(n)=[D(n(1)) D(n(2)) D(n(U))]

These C(n) and D(n) are then stored, after a one-numbering delay (or a unit-time delay) 33, in the storage 56, from which they are sent to the estimation means 54. The one-numbering delay is usually a time delay that is long enough for the estimation means to finish using current C(n) and D(n) in generating and outputting y_(T)(n), but short enough for getting the next C(n) and D(n) generated by the adjustment means available for the estimation means to use for processing the next orthogonal expansion or general orthogonal expansion from the expansion means.

Supervised learning means is described as follows: If a class label r_(T)(n)≠0 of x_(T)(n) from outside PU(n) is available and learning x_(T)(n) and r_(T)(n) is wanted, supervised learning means of the PU for adjusting at least one GECM (general expansion correlation matrix) performs supervised learning by receiving a GOE (general orthogonal expansion) {hacek over (x)}_(T)(n) generated by expansion means 2 and a label r_(T)(n)≠0 of x_(T)(n) provided from outside the PAM, using adjustment means 9 to adjust each ECM block in GECMs.

If a label r_(T)(n) of x_(T)(n) from outside the PU is unavailable but learning x_(T)(n) is wanted, unsupervised learning is performed by the PU. In this case, the lever (shown in position 48 in FIG. 13) should be in the position 50. The feature subvector x_(T)(n) is first processed by the expansion means 2, estimation means 54, conversion means 13 as in performing retrieval described above. The resultant ternary vector x{y_(T)(n)} is received, through the lever in position 50, and used by the adjustment means 9 as the label r_(T)(n) of x_(T)(n). The adjustment means 9 receives {hacek over (x)}_(T)(n) also and uses a method of adjusting ECMs such as those depicted in FIG. 5 and FIG. 6 and assembles the resultant ECMs, C(n(u)) and D(n(u)), u=1, . . . , U, into general ECMs

C(n)=[C(n(1)) C(n(2)) C(n(U))]

D(n)=[D(n(1)) D(n(2)) D(n(U))]

These C(n) and D(n) are then stored, after a one-numbering delay (or a unit-time delay) 33, in the storage 56, from which they are sent to the estimation means 54.

Unsupervised learning means is described as follows: If a label r_(T)(n) of x_(T)(n) from outside PU(n) is unavailable but learning x_(T)(n) is wanted, unsupervised learning means of the PU for adjusting at least one GECM (general expansion correlation matrix) performs unsupervised learning by receiving a GOE (general orthogonal expansion) {hacek over (x)}_(T)(n) generated by expansion means 2 and a ternary vector x{y_(T)(n)} generated by the conversion means 13 and using adjustment means 9 to adjust each ECM block in GECMs.

If no learning is to be performed by PU(n), the lever represented by a thick solid line with a solid dot is placed in the position 49, through which 0 is sent as the label r_(T)(n) of x_(T)(n) to the adjustment means 9, which then keeps C(n) and D(n) unchanged or stores the same C(n) and D(n) in the storage 56 after a one-numbering delay (or a unit-time delay).

FIG. 14 shows an example layer l of PUs (processing units) 15 in a PAM (probabilistic associative memory). The input to layer l is a feature vector x_(T) ^(l−1) from layer l−1, x_(T) ⁰ being an exogenous feature vector input to PAM. The feature subvectors, x_(T) ^(l−1)(n^(l)), n^(l)=1^(l), N^(l), of x_(T) ^(l−1) are input to the PUs, PU(n^(l)), n^(l)=1^(l), . . . , N^(l), respectively 15. Their possible output y_(T) ^(l)(n^(l)) and output x{y_(T) ^(l)(n^(l))}, n^(l)=1^(l), . . . , N^(l), are assembled 42 into vectors y_(T) ^(l) and x{y_(T) ^(l)}. Here y_(T) ^(l) is output if needed as indicated by the dashed arrow.

FIG. 15 shows an example hierarchical probabilistic associative memory (HPAM), which has L layers 5 of PUs. The feature vector input to layer l is x_(T) ^(l−1), and the vectors output from layer l are y_(T) ^(l) and x{y_(T) ^(l)}. The feature vector x_(T) ^(l−1) input to layer l comprises the vector x{y_(T) ^(l−1)} output from layer l−1. y_(T) ^(l)(n^(l)) is output if needed for each layer l. Note that the feature vector x_(T) ⁰ input to layer 1 is the exogenous feature vector x_(T) ⁰ input to the HPAM.

FIG. 16 shows an example temporal hierarchical probabilistic associative memory (THPAM), which is a probabilistic associative memory with 0-layer (i.e., same-layer) and 1-layer feedbacks, for processing sequences of exogenous feature vectors. The example THPAM has 4 layers 5 of PUs, four same-layer feedback paths 371, 372, 373, 374, and three 1-layer feedback paths 352, 353, 354. A small square box 33 containing the numeral 1 represents a delay device (e.g., storage/memory) that holds the output from a layer of PUs in processing an exogenous feature vector in a sequence until the output is included in the feature vector input to the same or a lower-ordered layer of PUs in processing the exogenous feature vector subsequent to said exogenous feature vector in the same sequence (i.e., one exogenous feature vector later). A box 39 enclosing ternary vectors preceding a layer of PUs indicates symbolically assembling or inclusion of the enclosed ternary vectors into a feature vector input to said layer.

For instance, in processing a sequence {x_(t) ⁰, t=1, 2, . . . } of exogenous feature vectors input to the PAM, the output x{y_(T−1) ³} of layer 3 of PUs in processing x_(T−1), which has been held in a delay device is included in the feature vector x_(T) ² input to the same layer,

x _(T) ² =[x′{y _(T−1) ⁴ } x′{y _(T) ² } x′{y _(T−1) ³}]′

through the feedback path 373 in processing x_(T), which is subsequent to x_(T−1) in the same sequence {x_(t), t=1, 2, . . . }. x{y_(T−1) ³} is also included in the feature vector input to layer 2 of PUs,

x _(T) ¹ =[x′{y _(T−1) ³ } x′{y _(T) ¹ } x′{y _(T−1) ²}]′

through the feedback path 353, in processing x_(T), which is subsequent to x_(T−1) in the same sequence {x_(t), t=1, 2, . . . }.

Before a new sequence of exogenous feature vectors is started to be processed, the feedbacked ternary vectors, which form the dynamical state of the THPAM, are all set equal to zero.

FIGS. 17 a-17 e show examples of rotation, translation and scaling in an RTS (rotation, translation and scaling) suite of a feature subvector index n, which is shown in FIG. 17 a. The components of n are the numberings (of a feature subvector) shown in the small circles within the retangular box 58. The cross with arrow heads indicate the orientation and position of n. FIG. 17 b shows a translation to the right. FIG. 17 c shows a rotation of the translation in FIG. 17 b. FIG. 17 d and FIG. 17 e show a compression and an expansion of the translation in FIG. 17 b. Five examples of translations of n are shown in FIG. 17 f.

FIG. 18 shows generation & summing of orthogonal expansions on a rotation/translation/scaling (RTS) suite of a subvector n(u) of n. Let Ω(n)={w(i), i=1, . . . , |Ω(n)|} be a set of rotations, translations, and scalings as described in FIG. 17. Ω(n) is called a RTS suite of n, and |Ω(n)| denotes the number of elements in Ω(n). Although w(i) is a rotation, translation, or scaling of n, this dependence on n is not indicated in the symbol w(i) for notational simplicity. As n is rotated, translated or scaled into w(i), n(u) as a subvector of n is rotated, translated or scaled into a subvector of w(i). This subvector of w(i) is denoted by n(u,w(i)). The set {n(u,w(i)), i=1, . . . , |Ω(n)|} of such subvectors of w(i), i=1, . . . , |Ω(n)|, is denoted by Ω(n(u)) and called a RTS suite of n(u). Note that |Ω(n(u))=|Ω(n)|. The set {x_(t)(n(u,w(i))), i=1, . . . , |Ω(n)|}, which is also denoted by {x_(t)(n(u,w)), w ∈ Ω(n)}, is called the RTS suite of x_(t)(n(u)) on Ω(n(u)). In generation and summing of orthogonal expansions on an RTS suite Ω(n(u)), vectors x_(t)(n(u,w)) in the RTS suite of x_(t)(n(u)) on Ω(n(u)), first go through orthogonal expansion 1. The resultant orthogonal expansions {hacek over (x)}_(t)(n(u,w(i))) are then added up to form the sum Σ_(w∈Ω(n)) {hacek over (x)}_(t)(n(u,w)) on the RTS suite Ω(n(u)) of n(u).

FIG. 19 shows expansion correlation matrices (ECMs), C(n(u)) and D(n(u)), on a rotation/translation/scaling (RTS) suite Ω(n(u)) of the subvector n(u) of a feature vector index n. C(n(u)) and D(n(u)) are defined by the pairs, (x_(l)(n(u,w)), r_(l)(n)), w ∈ Ω(n), t=1, . . . , T, with weight matrices W_(t)(n(u), T). Note that [C′(n(u)) D′(n(u))]′ can be viewed as a single expansion correlation matrix.

FIG. 20 shows, if the label r_(T)(n(u))≠0, how the expansion correlation matrices (ECMs), C(n(u)) and D(n(u)), defined in FIG. 19, are adjusted to learn a pair (x_(T)(n(u,w)), r_(T)(n)), for the weight matrix W_(t)(n(u), T)=λ^(T−t)I. If r_(T)(n)=0, the pair (x_(T)(n(u)), r_(T)(n)) is discarded. λ is a forgetting factor, and Λ is a scaling constant.

FIG. 21 shows, if the label r_(T)(n(u))≠0, how the expansion correlation matrices (ECMs), C(n(u)) and D(n(u)), defined in FIG. 19, are adjusted to learn a pair (x_(t)(n(u,w)), r_(t)(n)) for the weight matrix W_(t)(n(u), T=(1/√{square root over (T)})I. The number √{square root over (T)} is updated to √{square root over (T+1)} after a pair is learned. If r_(T)(n)=0, the pair (x_(T)(n(u)), r_(T)(n)) is discarded, and the number √{square root over (T)} is not updated.

FIG. 22 shows that a general orthogonal expansion {hacek over (x)}_(t)(n, Ω) on a rotation/translation/scaling (RTS) suite Ω(n) of an FSI n has sums Σ_(w∈Ω(n)){hacek over (x)}_(t)(n(u,w)), u=1, 2, . . . , U, of orthogonal expansions on an RTS suite Ω(n(u)), u=1, 2, . . . , U, as subvectors, general expansion correlation matrices, C(n) and D(n), on the RTS suite Ω(n) has expansion correlation matrices, C(n(u)) and D(n(u)), u=1, 2, . . . , U, on an RTS suite Ω(n(u)), u=1, 2, . . . , U, as submatrices. C(n(u)) and D(n(u)) are defined in FIG. 19. Note that n(u), u=1, . . . , U, are subvectors of the FSI n. It is understood that an orthogonal expansion of x_(t)(n) is a special case of a general orthogonal expansion of x_(t)(n) on Ω(n), and an expansion correlation matrix on the RTS suite Ω(n) (defined in FIG. 19, where n(u)=n) is a special case of a general expansion correlation matrix on the RTS suite Ω(n).

FIG. 23 shows how a GOE (general orthogonal expansion) {hacek over (x)}_(t)(n, Ω) on an RTS suite Ω(n), is generated. {hacek over (x)}_(t)(n, Ω) is defined in FIG. 22.

FIG. 24 shows an example processing unit (PU) on a feature subvector index n (PU(n)) that has the capability to recognize rotated, translated and scaled causes (e.g., objects) in an exogenous feature vector. In performing retrieval, a GOE (general orthogonal expansion) {hacek over (x)}_(T)(n) defined in FIG. 8 and a GOE {hacek over (x)}_(T)(n, Ω) on an RTS suite Ω(n) (defined in FIG. 22) are generated by the expansion means 18. {hacek over (x)}_(T)(n) is then processed by the estimation means 54, using the general expansion correlation matrices, C(n) and D(n) on Ω(n) (defined in FIG. 22), from the storage 56, into a representative of a probability distribution y_(T)(n) of a label r_(T)(n) of x_(T)(n). The conversion means 13 converts y_(T)(n) into a ternary vector x{y_(T)(n)}, which is an output of the PU. If a representative of a probability distribution of x_(T)(n) is needed for use outside the PU, y_(T)(n) is also output by the PU. The dashed line in the arrow 55 indicates “output as needed.”

If a label r_(T)(n)≠0 of x_(T)(n) from outside the PU is available for learning, and learning x_(T)(n) and r_(T)(n) is wanted, supervised learning is performed by the PU. In supervised learning, the label r_(T)(n)≠0 is received through a lever represented by a thick solid line with a solid dot in the position 48 by an adjustment means 9, which receives also {hacek over (x)}_(T)(n, Ω) and uses a method of adjusting expansion correlation matrices (ECMs) on an RTS suite Ω(n(u)) such as those depicted in FIG. 20 and FIG. 21 and assembles the resultant ECMs C(n(u)) and D(n(u)) on Ω(n(u)), u=1, . . . , U, into general ECMs on the RTS suite Ω(n)={Ω(n(1)), Ω(n(2)), . . . , Ω(n(U))},

C(n)=[C(n(1)) C(n(2)) C(n(U))]

D(n)=[D(n(1)) D(n(2)) D(n(U))]

These C(n) and D(n) are then stored, after a one-numbering delay (or a unit-time delay) 33, in the storage 56, from which they are sent to the estimation means 54.

Supervised learning means is described as follows: If a label r_(T)(n)≠0 of x_(T)(n) from outside PU(n) is available and learning x_(T)(n) and r_(T)(n) is wanted, supervised learning means of the PU for adjusting GECMs (general expansion correlation matrices) on Ω(n) performs supervised learning by receiving a GOE (general orthogonal expansion) {hacek over (x)}_(T)(n) generated by expansion means 2 and a label r_(T)(n)≠0 of x_(T)(n), provided from outside the PAM, and using adjustment means 9 to adjust each ECM block in GECMs on Ω(n).

If a label r_(T)(n) of x_(T)(n) from outside the PU is unavailable but learning x_(T)(n) is wanted, unsupervised learning is performed by the PU. In this case, the lever (shown in position 48 in FIG. 13) should be in the position 50. The feature subvector x_(T)(n) is first processed by the expansion means 2, estimation means 54, conversion means 13 as in performing retrieval described above. The resultant ternary vector x{y_(T)(n)} is received, through the lever in position 50, and used by the adjustment means 9 as the label r_(T)(n) of x_(T)(n). The adjustment means 9 receives {hacek over (x)}_(T)(n,Ω) also and uses a method of adjusting ECMs such as those depicted in FIG. 20 and FIG. 21 and assembles the resultant ECMs C(n(u)) and D(n(u)) on Ω(n(u)), u=1, . . . , U, into general ECMs on Ω(n),

C(n)=[C(n(1)) C(n(2)) . . . C(n(U))]

D(n)=[D(n(1)) D(n(2)) . . . D(n(U))]

These C(n) and D(n) on Ω(n) are then stored, after a one-numbering delay (or a unit-time delay) 33, in the storage 56, from which they are sent to the estimation means 54.

Unsupervised learning means is described as follows: If a label r_(T)(n) of x_(T)(n) from outside PU(n) is unavailable but learning x_(T)(n) is wanted, unsupervised learning means of the PU for adjusting GECMs (general expansion correlation matrices) on Ω(n) performs unsupervised learning by receiving a GOE (general orthogonal expansion) {hacek over (x)}_(T)(n, Ω) on Ω(n) generated by expansion means 18 and the ternary vector x{y_(T)(n)} (generated in processing {hacek over (x)}_(T)(n) in performing retrieval) as the label r_(T)(n) of x_(T)(n) and using adjustment means 9 to adjust each ECM block in GECMs on Ω(n).

If no learning is to be performed by PU(n), the lever represented by a thick solid line with a solid dot is placed in the position 49, through which 0 is sent as the label r_(T)(n) of x_(T)(n) to the adjustment means 9, which then keeps C(n) and D(n) unchanged or stores the same C(n) and D(n) in the storage 56 after a one-numbering delay (or a unit-time delay).

FIG. 25 is the same as FIG. 24 except that in addition to the GOE {hacek over (x)}_(T)(n, Ω) on the RTS suite Ω(n), the GOE {hacek over (x)}_(T)(n, Ω₁) on another RTS suite Ω₁(n) is generated by the expansion means 18. {hacek over (x)}_(T)(n, Ω₁) is sent to and processed by the estimation means 54. The purpose of processing {hacek over (x)}_(T)(n, Ω₁) instead of {hacek over (x)}_(T)(n) is to recognize object and causes that are more rotated, translated and scaled.

FIG. 26 shows a general orthogonal expansion {hacek over (x)}_(t) ^(l−1)(n) from FIG. 8, CGECMs (common general expansion correlation matrices), C^(l) and D^(l), on all RTS suites Ω(n^(l)) in layer l under the assumption that all FSIs n^(l) (including their subvectors n^(l)(u)) are translations of one another, and for simplicity and clarity in this disclosure, dim n^(l)(u)=m, for u=1, . . . U. C^(l)(n^(l)(u)) and D^(l)(n^(l)(u)) are ECMs on an RTS suite Ω(n^(l)(u)) defined in FIG. 19. 1^(l), 2^(l), . . . . , N^(l) are feature subvector indices for PUs in layer l. Note that [C^(l1) D^(l1)]′ can be viewed as a single CGECM on all RTS suites Ω(n^(l)) in layer l.

FIG. 27 is the same as FIG. 24 except that in FIG. 27, the GECMs [C^(l)′(n^(l)) D^(l)′(n^(l))]′ on Ω(n^(l)) generated by the adjustment means 9 are output from PU(n^(l)), and C^(l) and D^(l), which denote CGECMs (common general expansion correlation matrices) on all RTS suites Ω(n^(l)) in layer l, are input to PU(n^(l)) and after a unit-time delay (or one-numbering delay), stored in the storage 56.

FIG. 28 is the same as FIG. 25 except that in FIG. 28, the GECMs [C^(l)′(n^(l)) D^(l)′(n^(l))]′ on Ω(n^(l)) generated by the adjustment means 9 are output from PU(n^(l)), and C^(l) and D^(l), which denote CGECMs (common general expansion correlation matrices) on all RTS suites Ω(n^(l)) in layer l, are input to PU(n^(l)) and after a unit-time delay (or one-numbering delay), stored in the storage 56.

FIG. 29 how the GECMs [C^(l)′(n^(l)) D^(l)′(n^(l))]′ on Ω(n^(l)) for n^(l)=1^(l), 2^(l), . . . , N^(l), which are feature subvector indices for PUs in layer l, are output from the PUs and summed up 64 to obtain the CGECM [C^(l)′ D^(l)′]′ on all RTS suites Ω(n^(l)) in layer l and how the CGECM [C^(l)′ D^(l)′]′ is distributed to each PU in layer l.

FIG. 30 shows one way to combine y_(T)(m_(i))=2p_(T)(m_(i))−1, i=1, . . . , η, that are representatives of probability distributions of a common label r_(T), into a representative {circumflex over (P)}_(T) of a probability distribution of said common label. Here, m₁, m₂, . . . , m_(η) are FSIs, which may come from a single layer or from different layers of PUs, but the labels, r_(T)(m₁), r_(T)(m₂), . . . , r_(T)(m_(η)), of the feature vectors, x_(T)(m₁), x_(T)(m₂), . . . , x_(T)(m_(η)), on these FSIs are equal. Recall that p_(Tk)(m_(i)) denotes the probability that the k-th component r_(Tk) of the label r_(T) of x_(T)(m_(i)) is equal to 1, and that p_(Tk)(m_(i))=(y_(Tk)(m_(i))+1)/2, where y_(Tk)(m_(i)) is generated by the estimation means in the PU on m_(i). The R-dimensional vector {circumflex over (P)}_(T)=[{circumflex over (P)}_(T1) . . . {circumflex over (P)}_(TR)]′ is a representative of a probability distribution of r_(T).

FIGS. 31-38 are concerned with multiple adjustments of GECMs on an FSI n or on an RTS suite Ω(n) in PU(n) for each exogenous feature vector x_(t) ⁰. For an exogenous feature vector x_(t) ⁰, the multiple adjustments are completed between the arrival of x_(t) ⁰ and the arrival of x_(t+1) ⁰. The expansion means, estimation means, conversion means, and adjustment means in PU(n) all function multiple times for each exogenous feature vector. If a pseudo-random number generator is used in the conversion means, its outputs form bipolar binary pulse trains for each exogenous feature vector. They help eliminate possible pattern recognition errors due to randomness caused by the pseudo-random number generator.

FIG. 31 shows multiple generations of GOEs and multiple/group adjustments of GECMs on an FSI for each exogenous feature vector in supervised learning. In FIG. 31, the j-th feature subvector input to PU(n) is denoted by x_(t)(n,j) while an exogenous feature vector x_(t) ⁰ is received and held constant before the next exogenous feature vector is received. As usual, the GOE of x_(t)(n,j) is denoted by {hacek over (x)}_(t)(n,j). If the FSI n of PU(n) has subvectors (also called subvector indices), n(1), . . . , n(U), then {hacek over (x)}_(t)(n(1),j), . . . , {hacek over (x)}_(t)(n(U),j) are orthogonal expansions and are block components of the GOE {hacek over (x)}_(t)(n,j). C(n(u)) and D(n(u)) for u=1, . . . , U are defined in the figure and are the block columns of the GECMs, C(n) and D(n). In the definition of C(n(u)) and D(n(u)), labels r_(t)(n) are not zero, because if a label r_(T)(n) is zero, the corresponding pair {hacek over (x)}_(T)(n,j) and r_(T)(n) is discarded. The weight factor 1/J is an example. Other weight factor can be used. There are two ways to adjust C(n(u)) and D(n(u)). First, they are adjusted for each {hacek over (x)}_(t)(n,j), and hence are adjusted J times for each exogenous feature vector x_(l) ⁰. Second, C(n(u)) and D(n(u)) are adjusted only once, after {hacek over (x)}_(t)(n,j), j=1, . . . , J, are all received and

$\frac{1}{J}{\sum\limits_{j = 1}^{J}{{\overset{\Cup}{x}}_{\tau}\left( {n,j} \right)}}$

is obtained. In the first way, {hacek over (x)}_(t)(n,j), j=1, . . . , J, have been generated with different GECMs by PUs. In the second way, all PUs in the PAM keep their GECMs unchanged for j=1, . . . , J. The first way is called multiple adjustments of GECMs, and the second a group adjustment of the same. To faciliate multiple adjustments, we need a delay device in each PU that holds the GECMs for 1/J unit of time, before sends them to the storage 56.

FIG. 32 shows how a group adjustment of ECMs on an FSI for each exogenous feature vector in supervised learning is performed for W_(t)(n(u),j)=λ^(T−t)I. These ECMs form the block columns of the GECMs on n as shown in FIG. 31.

FIG. 33 shows multiple generations of GOEs and a group adjustment of GECMs on an FSI for each exogenous feature vector in unsupervised learning. For an exogenous feature vector x_(t) ⁰, the J feature subvectors, x_(t)(n,j), j=1, . . . , J, input to PU(n) share the same label to be generated by the PU. The ternary vector x{y_(t)(n,j)} that is generated from converting the most “informative” probability distribution p_(t)(n,j) should be used as the common label. The variance of a random variable with the probability distribution p_(lk)(n,j) is p_(lk)(n,j) (1−p_(lk)(n,j)). The sum of bariances,

${\sum\limits_{k = 1}^{K}{{p_{tk}\left( {n,j} \right)}\left( {1 - {p_{tk}\left( {n,j} \right)}} \right)}},$

is a measure of variability of p_(t)(n,j). Roughly speaking, the more variability a random variable has, the less information it contains. Therefore, the minimizer p_(t)(n,j*) of the sum of variances is most informative, and x{y_(t)(n,j*)} should be used as the common label of the J feature subvectors.

FIG. 34 shows how a group adjustment of GECMs on an FSI for each exogenous feature vector, which are shown in FIG. 33, is performed in unsupervised learning for W_(t)(n(u),j)=λ^(T−t)I.

FIG. 35 shows multiple generations of GOEs and multiple/group adjustment of GECMs on an RTS suite Ω(n) of an FSI n for each exogenous feature vector in supervised learning. In FIG. 35, the j-th feature subvector input to PU(n) is denoted by x_(t)(n,j) while an exogenous feature vector x_(t) ⁰ is received and held constant before the next exogenous feature vector arrives. The GOE on Ω(n) of x_(t)(n,j) is denoted by {hacek over (x)}_(t)(n,Ω,j). In the definition of C(n) and D(n), labels r_(t)(n) are not zero, because if a label r_(T)(n) is zero, the corresponding pair, {hacek over (x)}_(T)(n,j) and r_(T)(n), is discarded. The weight factor 1/J is an example. Other weight factor can be used.

FIG. 36 shows how a group adjustment of GECMs on an RTS suite Ω(n) of an FSI n for each exogenous feature vector in supervised learning is performed for W_(t)(n(u)j)=λ^(T−T)I.

FIG. 37 shows multiple generations of GOEs and multiple/group adjustment of GECMs on an RTS suite Ω(n) of an FSI n for each exogenous feature vector in unsupervised learning. For an exogenous feature vector x_(t) ⁰, the J feature subvectors, x_(t)(n,j), j=1, . . . , J, input to PU(n) share the same label to be generated by the PU. See the remark concerning j* in the description of FIG. 33.

FIG. 38 shows how a group adjustment of GECMs on an RTS suite Ω(n) of an FSI n for each exogenous feature vector, which are shown in FIG. 37, is performed in unsupervised learning for W_(t)(n(u),j)=λ^(T−t)I.

5 DESCRIPTION OF PREFERRED EMBODIMENTS

In the terminology of pattern recognition, a feature vector is a transformation of a measurement vector, whose components are measurements or sensor outputs. As a special case, the transformation is the identity transformation, and the feature vector is the measurement vector. Example measurement vectors are digital pictures, frames of a video, segments of speech, handwritten characters/words. This invention is mainly concerned with processing feature vectors and sequences of related feature vectors for detecting and recognizing spatial and temporal causes or patterns.

In this invention disclosure, a cortex-like learning machine, called a probabilistic associative memory (PAM), is disclosed that processes feature vectors or sequence of feature vectors, each feature vector being a ternary feature vector. A PAM can be viewed as a new neural network paradigm, a new type of learning machine, or a new type of pattern recognizer. A PAM is a network of processing units (PUs). In a multilayer PAM, the vector input to a layer is a feature vector, because it is the transformation of the feature vector input to the PAM, and in turn a transformation of the measurement vector. To distinguish feature vectors input to the first layer, which are also feature vectors input to the PAM, and feature vectors input to other layers of the PAM, the former are also called exogenous feature vectors. In a PAM with (delayed) feedback connections (or feedback means), called a recurrent PAM, a feature vector input to layer l comprises a vector output from layer l−1 and vectors output from PUs in other layers. For example, if there is a feedback connection to layer 1, then an exogenous feature vector is not the entire feature vector input to layer 1, but only a subvector of said entire feature vector.

A PU may comprise expansion means, estimation means, conversion means, adjustment means, feedback means, supervised learning means, unsupervised learning means, and/or storage. A vector input to a PU is usually a subvector of a feature vector input to the layer to which said PU belongs. The subvector input to a PU is called a feature subvector. A PU may have one or both of two functions—retrieving the label of a feature subvector from the memory (i.e., expansion correlation memories or general expansion correlation memories disclosed in this invention) and learning a feature subvector and its label that is either provided from outside the PU (in supervised learning) or generated by the PU itself (in unsupervised learning) and storing the learned knowledge in the memory. In performing retrieval, a feature subvector input to a PU is first expanded into a general orthogonal expansion by the expansion means. The orthogonal expansion is then processed by the estimation means, using the (general) expansion correlation matrices, from the storage, into a representative of a probability distribution of the label of said feature subvector. The conversion means converts said representative into a ternary vector, which is an output of the PU. If said representative is needed for use outside the PU, it is also output from the PU.

There are three types of PU according to how they learn supervised PUs, unsupervised PUs, and supervised/unsupervised PU. A supervised PU performs supervised learning, if a label of a feature subvector input to the PU is provided from outside the PU (or the PAM) and learning is wanted. An unsupervised PU performs unsupervised learning if a label of a feature subvector input to the PU is not provided from outside the PU but learning the feature subvector is wanted. A supervised/unsupervised PU can perform both supervised learning and unsupervised learning. Both supervised and unsupervised learning follow a Hebb rule of learning. During the process of learning, supervised or unsupervised, the PU is said to be performing learning.

A PU with a general masking matrix (to be described later on) has good generalization capability. A PU that has learned feature subvectors on a rotation/translation/scaling suite (to be described later on) has good capability for recognizing rotated, translated and scaled patterns.

In this invention disclosure, prime ′ denotes matrix transposition. Vectors whose components are 0's, 1's and −1's are called ternary vectors. Thus, the components of ternary vectors are elements of the ternary set, {−1, 0, 1}. Bipolar binary vectors are vectors whose components are elements of the binary set, {−1, 1}. Unipolar binary vectors are vectors whose components are elements of the binary set, {0, 1}. Since {−1, 1} and {0, 1} are subsets of the ternary set, bipolar and unipolar binary vectors are ternary vectors. For example, the bipolar binary vector [1 −1 1 −1]′ and the unipolar binary vector [1 0 1 0]′ are ternary vectors.

In the present invention disclosure, exogenous feature vectors input to a PAM are ternary vectors. 0's are usually used to represent unknown, unavailable, or corrupted part or parts of exogenous feature vectors. A vector whose components are the numberings (or subscripts) of the components of a feature vector that constitute a feature subvector and are ordered by the magnitudes of the numberings is called the feature subvector index of the feature subvector. For example, [v₂ v₄]′ is a feature subvector of the feature vector v=[v₁ v₂ v₃ v₄]′, and the feature subvector index of the feature subvector is [2 4]′. As usual, one of the subvectors of a vector is said vector itself. Labels in the present invention disclosure are also ternary vectors. 0's are usually used to represent unavailable, unknown or unused part or parts of labels. For instance, if we start out with labels with different dimensionalities (or different numbers of components) in an application, the dimensionalities of those with smaller dimensionalities can be increased by inserting 0's at the tops or bottoms as additional components so that all the labels have the same dimensionality in the application.

An orthogonal expansion and a general orthogonal expansion of a ternary vector are described in the next subsection. A general orthogonal expansion has, as its block column(s), at least one orthogonal expansion. An orthogonal expansion is a special case of a general orthogonal expansion. A sum or weighted sum of products of values of a vector-valued function evaluated at labels of feature subvectors and transposes of general orthogonal expansions of these feature subvectors (with the same feature subvector index) is called an expansion correlation matrix (on the feature subvector index). Note that if the vector-valued function of the label is one-dimensional, the feature subvector expansion correlation matrix is a vector, sometimes called a expansion correlation vector.

In the present invention disclosure, probabilities are usually subjective probabilities, and therefore variances; estimations; distributions and statements based on probabilities are usually those based on subjective probabilities, whether the word “subjective” is used or not, unless indicated otherwise.

There are many ways to convert discrete numbers into bipolar binary vectors. A standard way to convert a base-10 number into a unipolar binary number is to convert the base-10 representative into a base-2 representative. For instance, (51)₁₀=(110011)₂. A unipolar binary number can be converted into a bipolar binary vector by changing every 0 to −1. For instance, (110011)₂ is converted into [1 1 −1 −1 1 1]′. An example of converting a representative of a probability distribution, which is a R-vector with real-valued components, is illustrated in FIG. 11, wherein each component is approximated by a 3-component unipolar binary number before being converted into a bipolar binary number by changing every 0 to −1.

The Hamming distance between the standard unipolar binary representatives of two integers is not “consistent” with their real value distance in the sense that a larger Hamming distance may correspond to a smaller real-value distance. For instance, consider (10000)₂=(16)₁₀, (01111)₂=(15)₁₀, and (00000)₂=(°)₁₀. The Hamming distance between 10000 and 01111 is 5, and the real-value distance between 15 and 16 is only 1. However, the Hamming distance between 00000 and 01111 is 4, but the real-value distance between 15 and 0 is 15.

In some applications of the disclosed invention, the “consistency” with Hamming distance is important to ensure that the disclosed pattern classifier has better generalization ability. For “consistency” with Hamming distance, “grey level unipolar binary representatives” can be used. For instance, the integers, 6 and 4, are represented by the grey level representatives, 00111111 and 00001111, instead of the unipolar binary numbers, 110 and 100, respectively. The 8-dimensional bipolar binary vectors representing these grey level unipolar binary representatives are, respectively,

[−1 −1 1 1 1 1 1 1]′

[−1 −1 −1 −1 1 1 1 1]′

n-dimensional bipolar binary vectors are also called n-component bipolar binary vectors. For example, the above two 8-dimensional vectors are also called 8-component bipolar binary vectors. An obvious disadvantage of such a bipolar binary vector representative is the large number of components required.

For reducing this disadvantage, the well-known Gray encoding can be used (John G. Proakis, Digital Communication, Third Edition, McGraw-Hill, 1995). The Gray code words of two adjacent integers differ by one component. For example, the Gray code words of the integers, 0 to 15, are, respectively, 0000, 0001, 0011, 0010, 0110, 0111, 0101, 0100, 1100, 1101, 1111, 1110, 1010, 1011, 1001, 1000. The corresponding bipolar binary vector representatives are easily obtained as before. For example, the Gray code word of the integer 12 is 1010, and the bipolar binary vector representative of it is [1, −1, 1, −1]. Gray code is not completely “consistent” with the Hamming distance. For instance, the Hamming distance between the Gray code words of the integers, 0 and 2, is 2, but the Hamming distance between those of the integers, 0 and 3, is only 1. However, compared with the grey level representative, the representative from Gray encoding requires much smaller number of components.

There are other methods of transforming measurement vectors or feature vectors that are not ternary vectors into bipolar binary feature vectors, which can then be transformed into ternary feature vectors. Feature vectors used by PAMs are ternary feature vectors.

For simplicity and clarity, all feature vectors are ternary feature vectors in the rest of this invention disclosure unless indicated otherwise; column and row vectors are special matrices and also called matrices; and a matrix is considered to consist of row or column vector(s).

5.1 Orthogonal Expansion of Ternary Vectors

We now show how ternary vectors are expanded into orthogonal ternary vectors by a method recently discovered by this inventor. In this invention disclosure, the transpose of a matrix or a vector is denoted by an apostrophe ' (i.e., a prime).

Given an m-dimensional ternary vector v=[v₁ v₂ . . . v_(m)]′, the first-stage expansion of v is defined as {hacek over (v)}(1)=[1 v₁]′, and the second-stage expansion is defined as

$\begin{matrix} {{\overset{\Cup}{v}\left( {1,2} \right)} = \begin{bmatrix} {\overset{\Cup}{v}}^{\prime} & (1) & {v_{2}{\overset{\Cup}{v}}^{\prime}} & (1) \end{bmatrix}^{\prime}} \\ {= \begin{bmatrix} 1 & v_{1} & v_{2} & {v_{2}v_{1}} \end{bmatrix}^{\prime}} \end{matrix}$

In general, the (j+1)-th-stage expansion is recursively defined as

{hacek over (v)}(1, . . . , j+1)=[{hacek over (v)}′(1, . . . ,j) v _(j+1) {hacek over (v)}′(1, . . . ,j)]′  (1)

The m-th stage expansion, which includes all the different powers of the components of v, is a 2^(m)-dimensional ternary vector:

{hacek over (v)}=[1 v₁ v₂ v₂v₁ v₃ v₃v₁ v₃v₂ v₃v₂v₁ . . . v_(m) . . . v₁]′  (2)

which is called the orthogonal expansion of v. Reordering the components of v in accordance with the powers of the components, we obtain an alternative orthogonal expansion:

{hacek over (v)}=[1 v₁ . . . v_(m) v₁v₂ . . . v₁v_(m) v₂v₃ . . . v₁ . . . v_(m)]′

which can also be used in this invention disclosure. In fact, many other orthogonal expansions of v are possible by different orderings of the components, but are all denoted by {hacek over (v)}. The use of the same symbol {hacek over (v)} is not expected to cause confusion. The components of any orthogonal expansion of v form the set,

{v ₁ ^(i) ¹ v ₂ ^(i) ² . . . v _(m) ^(i) ^(m) |i _(j) ∈{0, 1}, j=1, 2, . . . , m}

which has 2^(m) elements. The components in {hacek over (v)} are actually the terms in the expansion of (1+v₁) (1+v₂) (1+v_(m)). Subvectors of orthogonal expansions are sometimes used instead for reducing storage or memory space and/or computation requirements. THEOREM 1. Let a=[a₁ . . . a_(m)]′ and b=[b₁ . . . b_(m)]′ be two m-dimensional ternary vectors. Then the inner product {hacek over (a)}′{hacek over (b)} of their orthogonal expansions, {hacek over (a)} and {hacek over (b)}, can be expressed as follows:

${{\overset{\Cup}{a}}^{\prime}\overset{\Cup}{b}} = {\prod\limits_{j = 1}^{m}\; \left( {1 - {a_{j}b_{j}}} \right)}$

The following properties are immediate consequences of this formula:

1. If a_(k)b_(k)=−1 for some k ∈ {1, . . . , m}, then {hacek over (a)}′{hacek over (b)}=0.

2. If a_(k)b_(k)=0 for some k in {1, . . . , m}, then

${{\overset{\Cup}{a}}^{\prime}\overset{\Cup}{b}} = {\prod\limits_{{j = 1},{j \neq k}}^{m}\; {\left( {1 - {a_{j}b_{j}}} \right).}}$

3. If {hacek over (a)}′{hacek over (b)}≠0, then {hacek over (a)}′{hacek over (b)}=2^(a′b).

4. If a and b are bipolar binary vectors, then {hacek over (a)}′{hacek over (b)}=0 if a≠b; and {hacek over (a)}′{hacek over (b)}=2^(m) if a=b.

Proof. Applying the recursive formula (1), we obtain

${{{\overset{\Cup}{a}}^{\prime}\left( {1,\ldots \mspace{14mu},{j + 1}} \right)}{\overset{\Cup}{b}\left( {1,\ldots \mspace{14mu},{j + 1}} \right)}} = {{\left\lbrack {{{\overset{\Cup}{a}}^{\prime}\left( {1,\ldots \mspace{14mu},j} \right)}a_{j + 1}{{\overset{\Cup}{a}}^{\prime}\left( {1,\ldots \mspace{14mu},j} \right)}} \right\rbrack \left\lbrack {{{\overset{\Cup}{b}}^{\prime}\left( {1,\ldots \mspace{14mu},j} \right)}b_{j + 1}{{\overset{\Cup}{b}}^{\prime}\left( {1,\ldots \mspace{14mu},j} \right)}} \right\rbrack}^{\prime} = {{{{{\overset{\Cup}{a}}^{\prime}\left( {1,\ldots \mspace{14mu},j} \right)}{\overset{\Cup}{b}\left( {1,\ldots \mspace{14mu},j} \right)}} + {a_{j + 1}b_{j + 1}{{\overset{\Cup}{a}}^{\prime}\left( {1,\ldots \mspace{14mu},j} \right)}{\overset{\Cup}{b}\left( {1,\ldots \mspace{14mu},j} \right)}}} = {{{\overset{\Cup}{a}}^{\prime}\left( {1,\ldots \mspace{14mu},j} \right)}{\overset{\Cup}{b}\left( {1,\ldots \mspace{14mu},j} \right)}\left( {1 + {a_{j + 1}b_{j + 1}}} \right)}}}$

It follows that {hacek over (a)}′{hacek over (b)}=(1+a₁b₁) (1+a₂b₂) (1+a_(m)b_(m)), which is equal to 0 if at least one of a_(i)b_(i) is equal to −1, and equal to 2^(m) otherwise. Hence, {hacek over (a)} and {hacek over (b)} are orthogonal if and only if a≠b. This completes the proof.

We remark that if some components of a are set equal to zero to obtain a vector c and the nonzero components of c are all equal to their corresponding components in b, then we still have {hacek over (c)}′{hacek over (b)}≠0. This property is used by learning machines disclosed herein to learn and recognize corrupted and incomplete patterns and to facilitate generalization on these patterns.

The following notations and terminologies are used in this invention disclosure: For v=[v₁ v₂ . . . v_(m)]′ considered above, let n=[n₁ . . . n_(k)]′ be a vector whose components are different integers from the set {1, . . . , m} such that 1≦n₁< . . . <n_(k)≦m. The vector v(n)=[v_(n) ₁ . . . v_(n) _(k) ]′ is a subvector, called a k-component or k-dimensional subvector, of the vector v. The vector n is called a subvector index. v(n) is said to be on the subvector index n or have the subvector index n. {hacek over (v)}(n) denotes the orthogonal expansion of v(n).

5.2 Expansion Correlation Matrices

An embodiment of the present invention, a PAM (probabilistic associative memory), has L layers of processing units (PUs) shown in FIG. 15. A vector input into layer l is a ternary vector and denoted by x_(t) ^(t−1) where t denotes a numbering of the input vector. Note that feature vectors x_(t) ^(l−1) with different numberings l are not necessarily different vectors. A vector x_(l) ⁰ input to layer 1 is a vector input to the PAM. For l≧2, the vector x_(t) ^(l−1) input to layer l is a vector output from layer l−1. Vectors x_(t) ^(l−1), l=1, . . . , L+1, when used as input vectors, are called feature vectors. To distinguish feature vectors x_(t) ⁰ input to a PAM and other feature vectors, x_(t) ^(l−1), l>1, the former are called exogenous feature vectors. For notational simplicity, the superscript l−1 in x_(t) ^(l−1) and dependencies on l−1 or l in other symbols are sometimes suppressed in the following when no confusion is expected.

Let x_(t), t=1, 2, . . . , denote a sequence of M-dimensional feature vectors x_(t)=[x_(t1) . . . x_(tM)]′, whose components are ternary numbers. The feature vectors x_(t), t=1,2, . . . , are not necessarily different. The ternary entry x_(tm) is called the m-th component of the feature vector x_(t). Let n=[n₁ . . . n_(k)]′ be a subvector [1 . . . M]′ such that n₁< . . . <n_(k). The subvector x_(t)(n)=[x_(tn) ₁ . . . x_(tn) _(k) ]′ is a feature subvector of the feature vector x_(t). n is called a feature subvector index (FSI), and x_(t)(n) is said to be a feature subvector on the FSI n or have the FSI n. Each PU is associated with a fixed FSI n and denoted by PU(n). Using these notations, the sequence of subvectors of x_(t), t=1,2, . . . , that is input to PU(n) is x_(t)(n), t=1, 2, . . . . An example of a group of ternary “pixels” that is identified with a feature subvector index n is shown in FIG. 3. An FSI n of a PU usually has subvectors, n(u), u=1, . . . , U, on which subvectors x_(t)(n(u)) of x_(t)(n) are separately processed by PU(n) at first. The subvectors, n(u), u=1, . . . , U, are not necessarily disjoint, and their components are usually randomly selected from those of n. An example of such a subvector n(u) is shown in FIG. 3 and indicated by 46.

Let the label of x_(t)(n) be denoted by r_(t)(n), which is an R-dimensional vector. If R is 1, r_(t)(n) is real-valued. All subvectors, x_(t)(n(u)), u=1, . . . , U, of x_(t)(n) share the same label r_(t)(n). In supervised learning by PU(n), r_(t)(n) is provided from outside the PAM, and in unsupervised learning by PU(n), r_(t)(n) is generated by the PU itself.

The pairs (x_(t)(n(u)), r_(t)(n)), t=1, 2, . . . , are learned by the PU to form two of expansion correlation matrices, D(n(u)), C(n(u)), A(n(u)), B(n(u)) on n(u). After the first T pairs are learned, these matrices are

$\begin{matrix} {{D\left( {n(u)} \right)} = {\Lambda {\sum\limits_{t = 1}^{T}\; {{W_{t}\left( {{n(u)},T} \right)}{r_{t}(n)}{{\overset{\Cup}{x}}_{t}^{\prime}\left( {n(u)} \right)}}}}} & (3) \\ {{C\left( {n(u)} \right)} = {\Lambda {\sum\limits_{t = 1}^{T}\; {{W_{t}\left( {{n(u)},T} \right)}I{{\overset{\Cup}{x}}_{t}^{\prime}\left( {n(u)} \right)}}}}} & (4) \\ {{A\left( {n(u)} \right)} = {\frac{1}{2}\Lambda {\sum\limits_{t = 1}^{T}\; {{W_{t}\left( {{n(u)},T} \right)}\left( {I + {r_{t}(n)}} \right){{\overset{\Cup}{x}}_{t}^{\prime}\left( {n(u)} \right)}}}}} & (5) \\ {{B\left( {n(u)} \right)} = {\frac{1}{2}\Lambda {\sum\limits_{t = 1}^{T}\; {{W_{t}\left( {{n(u)},T} \right)}\left( {I - {r_{t}(n)}} \right){{\overset{\Cup}{x}}_{t}^{\prime}\left( {n(u)} \right)}}}}} & (6) \end{matrix}$

where {hacek over (x)}_(t)(n(u)) are orthogonal expansions of x_(t)(n(u)), I=[1 . . . 1]′ with R components, Λ is a scaling constant that is selected to keep all numbers involved in an application of a PAM manageable, W_(t)(n(u),T) is a weight matrix, which is usually an diagonal matrix, diag(w_(t1)(n(u),T) w_(t2)(n(u),T) . . . w_(tR)(n(u),T)), that is selected to place emphases on components of the label, place emphases on (x_(t)(n(u)), r_(t)(n)) of different numberings t, and keep the entries in the ECMs bounded. For example, W_(t)(n(u),T)=λ^(T−t)2^(−dim n(u))h(n(u)) I, where λ (0<λ<1) is a forgetting factor, 2^(−dim n(u)) eliminates the constant 2^(dim n(u)) arising from {hacek over (x)}_(t)′(n(u)) {hacek over (x)}_(l)(n(u))=2^(dim n(u)), and h(n(u)) assigns emphases to subvectors x_(t)(n(u)) on n(u). There are many other possible weight matrices, depending on applications of the present invention.

Any two of the above four expansion correlation matrices (ECMs) can be obtained from the other two. We usually use D(n(u)) and C(n(u)) in this invention disclosure. They can be combined into one ECM [C′(n(u)) D′(n(u))]′. If W_(t)(n(u),T)=w_(t)(n(u),T) I, where w_(t)(n(u),T) is a real-valued function, only one row of C(n(u)) is needed. In this case, [C′(n(u)) D′(n(u))]′ has (R+1) rows.

ECMs, D(n(u)) and C(n(u)), can be adjusted to learn a pair (x_(T)(n(u)), r_(T)(n)). For example, if W_(t)(n(u),T)=λ^(T−t)I, the ECMs are adjusted as follows: If r_(T)(n)≠0, D(n(u)) and C(n(u)) are replaced respectively with λD(n(u))+Λr_(T)(n){hacek over (x)}_(t)′(n(u)) and λC(n(u))+Λ{hacek over (x)}_(t)′(n(u)), which are each a weighted sum of a ECM and r_(T)(n){hacek over (x)}_(t)′(n(u)) or {hacek over (x)}_(t)′(n(u)). If r_(T)(n)=0, then D(n(u)) and C(n(u)) are unchanged. Note that W_(t)(n(u),T) is a diagonal matrix, and C(n(u)) is a row vector here. This adjustment of the ECMs is shown in FIG. 5.

If W_(t)(n(u),T)=I/√{square root over (T)}, the ECMs are adjusted as follows: If r_(T)(n)≠0, D(n(u)) and C(n(u)) are replaced respectively with (√{square root over (T)}D(n(u))+Λr_(T)(n){hacek over (x)}_(t)′(n(u)))/√{square root over (T+1)} and (√{square root over (T)}C(n(u))+Λ{hacek over (x)}_(t)′(n(u)))/√{square root over (T+1)}, which are each a weighted sum of a ECM and r_(T)(n){hacek over (x)}_(t)′(n(u)) or {hacek over (x)}_(t)′(n(u)), and ⇄{square root over (T)} is replaced with √{square root over (T+1)}. If r_(T)(n)=0, then D(n(u)) and C(n(u)) are unchanged. Note that W_(t)(n(u),T) is a diagonal matrix, and C(n(u)) is a row vector here. This adjustment of the ECMs is shown in FIG. 6.

Orthogonal expansions (OEs) {hacek over (x)}_(t)(n(u)) and ECMs, D(n(u)), C(n(u)), A(n(u)), B(n(u)), u=1, . . . , U, are assembled into a general orthogonal expansion (GOE) {hacek over (x)}_(t)(n) and general expansion correlation matrices (GECMs), D(n), C(n), A(n), B(n), for PU(n) (the PU on the feature vector n) as follows:

{hacek over (x)} _(t)(n)=[{hacek over (x)} _(t)′(n(1)) {hacek over (x)} _(t)′(n(2)) . . . {hacek over (x)}_(t)′(n(U))]′  (7)

D(n)=[D(n(1)) D(n(2)) . . . D(n(U))]  (8)

C(n)=[C(n(1)) C(n(2)) . . . C(n(U))]  (9)

A(n)=[A(n(1)) A(n(2)) . . . A(n(U))]  (10)

B(n)=[B(n(1)) B(n(2)) . . . B(n(U))]  (11)

The GOE {hacek over (x)}_(t)(n) and the GECMs, D(n) and C(n), are shown in FIG. 8.

5.3 Representatives of Probability Distributions

Let us define the symbols a_(T)(n(u)), b_(T)(n(u)), c_(T)(n(u)), d_(T)(n(u)) by

a _(T)(n(u)):=A(n(u)){hacek over (x)}_(T)(n(u))   (12)

b _(T)(n(u)):=B(n(u)){hacek over (x)}_(T)(n(u))   (13)

c _(T)(n(u)):=C(n(u)){hacek over (x)}_(T)(n(u))   (14)

d _(T)(n(u)):=D(n(u)){hacek over (x)}_(T)(n(u))   (15)

and the symbols a_(T)(n), b_(T)(n), c_(T)(n), d_(T)(n) by

$\begin{matrix} \begin{matrix} {{a_{\tau}(n)}:={{A(n)}{{\overset{\Cup}{x}}_{\tau}(n)}}} \\ {= {\sum\limits_{u = 1}^{U}\; {{A\left( {n(u)} \right)}{{\overset{\Cup}{x}}_{\tau}\left( {n(u)} \right)}}}} \\ {= {\sum\limits_{u = 1}^{U}\; {a_{\tau}\left( {n(u)} \right)}}} \end{matrix} & (16) \\ \begin{matrix} {{b_{\tau}(n)}:={{B(n)}{{\overset{\Cup}{x}}_{\tau}(n)}}} \\ {= {\sum\limits_{u = 1}^{U}\; {{B\left( {n(u)} \right)}{{\overset{\Cup}{x}}_{\tau}\left( {n(u)} \right)}}}} \\ {= {\sum\limits_{u = 1}^{U}\; {b_{\tau}\left( {n(u)} \right)}}} \end{matrix} & (17) \\ \begin{matrix} {{c_{\tau}(n)}:={{C(n)}{{\overset{\Cup}{x}}_{\tau}(n)}}} \\ {= {\sum\limits_{u = 1}^{U}\; {{C\left( {n(u)} \right)}{{\overset{\Cup}{x}}_{\tau}\left( {n(u)} \right)}}}} \\ {= {\sum\limits_{u = 1}^{U}\; {c_{\tau}\left( {n(u)} \right)}}} \end{matrix} & (18) \\ \begin{matrix} {{d_{\tau}(n)}:={{D(n)}{{\overset{\Cup}{x}}_{\tau}(n)}}} \\ {= {\sum\limits_{u = 1}^{U}\; {{D\left( {n(u)} \right)}{{\overset{\Cup}{x}}_{\tau}\left( {n(u)} \right)}}}} \\ {= {\sum\limits_{u = 1}^{U}{d_{\tau}\left( {n(u)} \right)}}} \end{matrix} & (19) \end{matrix}$

where {hacek over (x)}_(t)(n) is a general orthogonal expansion (GOE) and D(n), C(n), A(n) and B(n) are general expansion correlation matrices (GECMs) for PU(n). It is easy to see that d_(T)(n(u))=2a_(T)(n(u))−c_(T)(n(u)), and d_(T)(n)=2a_(T)(n)−c_(T)(n).

To illustrate the use of a_(T)(n), b_(T)(n), c_(T)(n), d_(T)(n), two examples are given in the following:

(1). Assume that x_(t)(n) and x_(T)(n) are bipolar vectors, and W_(t)(n(u),T)=I/√{square root over (T)}. By Theorem 1, {hacek over (x)}_(t)′(n(u)) {hacek over (x)}_(T)(n(u))=0 or {hacek over (x)}_(t)′(n(u)) {hacek over (x)}_(T)(n(u))=2^(dim n(u)), depending on whether x_(k)(n(u))≠x_(T)(n(u)) or x_(k)(n(u))=x_(T)(n(u)). It follows that

a _(Tj)(n(u))=Λ2^(dim n(u)) |G _(Tj)(n(u), +)|/√{square root over (T)}  (20)

b _(Tj)(n(u))=Λ2^(dim n(u)) |G _(Tj)(n(u), −)|/√{square root over (T)}  (21)

G _(Tj)(n(u), +)={t ∈[0, T]|x _(t)(n(u))=x _(T)(n(u)), r _(tj)(n)=1}  (22)

G _(Tj)(n(u), −)={t ∈[0, T]|x _(t)(n(u))=x _(T)(n(u)), r _(tj)(n)=−1}  (23)

where |G_(Tj)(n(u), +)| and |G_(Tj)(n(u), −)| are the numbers of elements in the sets G_(Tj)(n(u), +) and G_(Tj)(n(u), −), respectively. If a_(Tj)(n(u))=b_(Tj)(n(u)) (i.e., c_(Tj)(n(u))=0, or c_(Tj)(n(u))≠0, but a_(Tj)(n(u))=b_(Tj)(n(u))), the ECMs for n(u) have no information or no preference about the value of the component r_(Tj)(n) of the label r_(T)(n) of the feature subvector x_(T)(n).

It is follows that

${a_{\tau \; j}(n)} = {\frac{\Lambda}{\sqrt{T}}{\sum\limits_{u = 1}^{U}\; {2^{\dim \mspace{11mu} {n{(u)}}}{{G_{\tau \; j}\left( {{n(u)}, +} \right)}}}}}$ ${b_{\tau \; j}(n)} = {\frac{\Lambda}{\sqrt{T}}{\sum\limits_{u = 1}^{U}\; {2^{\dim \mspace{11mu} {n{(u)}}}{{G_{\tau \; j}\left( {{n(u)}, -} \right)}}}}}$ ${c_{\tau \; j}(n)} = {\frac{\Lambda}{\sqrt{T}}{\sum\limits_{u = 1}^{U}\; {2^{\dim \mspace{11mu} {n{(u)}}}\left( {{{G_{\tau \; j}\left( {{n(u)}, +} \right)}} + {{G_{\tau \; j}\left( {{n(u)}, -} \right)}}} \right)}}}$ d_(τ j)(n) = 2a_(τ j)(n) − c_(τ j)(n)

(This shows that if dim n(u₁)=dim n(u₂)+k, then a_(Tj)(n(u₁)) and c_(Tj)(n(u_(i))) are weighted 2^(k) times as much as a_(Tj)(n(u₂)) and c_(Tj)(n(u₁)) in a_(Tj)(n) and c_(Tj)(n). This weighting can be changed by choosing h(n(u)) in W_(t)(n(u),T)=λ^(T−t)2^(−dim n(u))h(n(u))I.)

For simplicity and clarity, assume that dim n(u), u=1, . . . , U, are all the same. If c_(Tj)(n)≠0, then

$\begin{matrix} {\frac{a_{\tau \; j}(n)}{c_{\tau \; j}(n)} = \frac{\sum\limits_{u = 1}^{U}\; {{G_{\tau \; j}\left( {{n(u)}, +} \right)}}}{\sum\limits_{u = 1}^{U}\; \left( \left. {{G_{\tau \; j}\left( {{n(u)}, +} \right.} + {{G_{\tau \; j}\left( {{n(u)}, -} \right)}}} \right) \right.}} & (24) \end{matrix}$

can be viewed as a probability that r_(Tj)(n)=1. This probability is a subjective probability, because it is based on “experience” represented by the GECMs C(n) and A(n), or C(n) and D(n)=2A(n)−C(n), which are learned from (or constructed with) (x_(t)(n(u)), r_(t)(n)), t=1, 2, . . . , T. (2). Assume that all x_(t)(n) and x_(T)(n) are bipolar binary vectors, and W_(t)(n(u),T)=Λ^(T−t)I.

${a_{\tau \; j}\left( {n(u)} \right)} = {\Lambda {\sum\limits_{t \in {G_{\tau \; j}{({{n{(u)}}, +})}}}\; {2^{\dim \mspace{11mu} {n{(u)}}}\lambda^{T - t}}}}$ ${b_{\tau \; j}\left( {n(u)} \right)} = {\Lambda {\sum\limits_{t \in {G_{\tau \; j}{({{n{(u)}}, -})}}}\; {2^{\dim \mspace{11mu} {n{(u)}}}\lambda^{T - t}}}}$

Assume further that dim n(u), u=1, . . . , U are all the same. Then if c_(Tj)(n)≠0,

$\begin{matrix} {\frac{a_{\tau \; j}(n)}{c_{\tau \; j}(n)} = \frac{\sum\limits_{u = 1}^{U}\; {\sum\limits_{t \in {G_{\tau \; j}{({{n{(u)}}, +})}}}\; \lambda^{T - t}}}{\sum\limits_{u = 1}^{U}\; {\sum\limits_{t \in {{G_{\tau \; j}{({{n{(u)}}, +})}}\bigcup{G_{\tau \; j}{({{n{(u)}}, -})}}}}\; \lambda^{T - t}}}} & (25) \end{matrix}$

can be viewed as a subjective probability that the label r_(Tj)(n)=1, based on the GECMs C(n) and A(n), or C(n) and D(n)=2A(n)−C(n), which are constructed with pairs (x_(t)(n(u)), r_(t)(n)), t=1, 2, . . . , T. The forgetting factor λ de-emphasizes past pairs gradually.

In both of the above examples, W_(t)(n(u),T) is a diagonal matrix with equal diagonal entries. In this case, all components c_(Tj)(n) of c_(T)(n) are equal and all rows of C(n) are equal. Therefore, only one row of C(n), say C₁(n), and one component of c_(T)(n), say c_(T1)(n), are sufficient. If only C₁(n) is used, it is also denoted by C(n).

In general, the ratio a_(Tj)(n)/c_(Tj)(n) can be viewed as a subjective probability that r_(Tj)(n)=1 based on the pairs (x_(t)(n), r_(t)(n)) that have been learned by PU(n) and the weight matrices W_(t)(n(u),T). All the statements concerning a probability in this invention disclosure are statements concerning a subjective probability, and the word “subjective” is usually omitted. If c_(Tj)(n)≠0, then a_(Tj)(n)/c_(Tj)(n) is the probability p_(Tj)(n) that the j-th component r_(Tj)(n) of the label r_(T)(n) of x_(T)(n) is +1 based on D(n) and C(n). If c_(Tj)(n)=0, then we set p_(Tj)(n)=½. The vector

p _(T)(n)=[p _(T1)(n) p _(T2)(n) . . . p_(TR)(n)]′

is a representative of a probability distribution of the label r_(T)(n) of the feature subvector x_(T)(n) input to PU(n). Since D(n)=2A(n)−C(n), if c_(Tj)(n)≠0, the ratio d_(Tj)(n)/c_(Tj)(n) is equal to 2p_(Tj)(n)−1. If c_(Tj)(n)=0, set 2p_(Tj)(n)−1=0. Denote 2p_(Tj)(n)−1 by y_(Tj)(n). Then the vector y_(T)(n)=2p_(T)(n)−I satisfies

$\begin{matrix} {{y_{\tau}(n)} = \begin{bmatrix} {{2{p_{\tau \; 1}(n)}} - 1} & \cdots & {{2{p_{\tau \; R}(n)}} - 1} \end{bmatrix}^{\prime}} \\ {= \begin{bmatrix} {{d_{\tau \; 1}(n)}/{c_{\tau \; 1}(n)}} & {{d_{\tau \; 2}(n)}/{c_{\tau \; 2}(n)}} & \cdots & {{d_{\tau \; R}(n)}/{c_{\tau \; R}(n)}} \end{bmatrix}^{\prime}} \end{matrix}$

and is also a representative of a probability distribution of the label r_(T)(n) of the feature subvector x_(T)(n). Here, I=[1 1 . . . 1]′.

5.4 Masking Matrices

Let a subvector x_(T)(n(u)) be a slightly different (e.g., corrupted, modified, deviated) version of x_(ξ)(n(u)), which is one of the subvectors, x_(t)(n(u)), t=1, 2, . . . , T, stored in ECMs, D(n(u)) and C(n(u)) (or any two of the four ECMs, D(n(u)), C(n(u)), A(n(u)) and B(n(u)), on n(u)). Assume that x_(T)(n(u)) is very different from other subvectors stored in the ECMs. Since {hacek over (x)}_(ξ)′(n(u)){hacek over (x)}_(T)(n(u))=0, the information stored in D(n(u)) and C(n(u)) about the label r_(ξ)(n) cannot be obtained from d(n(u))=D(n(u)){hacek over (x)}_(T)(n(u)) and c(n(u))=C(n(u)){hacek over (x)}_(T)(n(u)). This is viewed as failure of d(n(u)) and c(n(u)) or the ECMs to generalize or adequately generalize on x_(T)(n(u)). Because of property 2 in Theorem 1, if the corrupted components in x_(T)(n(u)) are set equal to zero, then the information stored in the ECMs about the label r_(ξ)(n) can be obtained in part from the remaining components of x_(T)(n(u)). This observation motivated masking matrices described in this section.

Let us denote the vector v=[v₁ v₂ . . . v_(n)]′ with its i₁-th, i₂-th, . . . , and i_(j)-th components set equal to 0 by v(i₁ ⁻, i₂ ⁻, . . . , i_(j) ⁻), where 1≦i₁<i₂< . . . <i_(j)≦n. For example, if v=[1 −1 −1 1]′, then v(2⁻,4⁻)=[1 0 −1 0]′. Denoting the n-dimensional vector [1 1 . . . 1]′ by I and denoting the orthogonal expansion of v(i₁ ⁻,i₂ ⁻, . . . ,i_(j) ⁻) by {hacek over (v)}(i₁, i₂, . . . , i_(j)), we note that v(i₁, i₂, . . . , i_(j))=diag (I(i₁, i₂, . . . i_(j))) v and {hacek over (v)}(i₁, i₂, . . . , i_(j))=diag ({hacek over (I)}(i₁, i₂, . . . , i_(j))) {hacek over (v)}, where {hacek over (v)}(i₁i₂, . . . , i_(j)) and {hacek over (I)}(i₁, i₂, . . . , i_(j)) denote the orthogonal expansions of v(i₁ ⁻, i₂ ⁻, . . . , i_(j) ⁻) and I(i₁ ⁻, i₂ ⁻, . . . , i_(j) ⁻) respectively (not the orthogonal expansions of v and I with their i₁-th, i₂-th, . . . , and i_(j)-th components set equal to 0).

Using these notations, a feature subvector x(n(u)) with its i₁-th, i₂-th, . . . , and i_(j)-th components set equal to 0 is x_(t)(n(u)) (i₁, i₂, . . . , i_(j)) and the orthogonal expansion of x_(t)(n(u)) (i₁, i₂, . . . , i_(j)) is diag({hacek over (I)}(i₁ ⁻, i₂ ⁻, . . . , i_(j) ⁻)) {hacek over (x)}_(t)(n (u)). Hence, the matrix diag({hacek over (I)}(i₁ ⁻, i₂ ⁻, . . . , i_(j) ⁻)), as a matrix transformation, sets the i₁-th, i₂-th, . . . , and i_(j)-th components of x_(t)(n(u)) equal to zero in transforming {hacek over (x)}_(t)(n(u)) (i e., diag({hacek over (I)}(i₁ ⁻, i₂ ⁻)) {hacek over (x)}_(t)(n(u))). Therefore, diag({hacek over (I)}(i₁ ⁻, i₂ ⁻, . . . , i_(j) ⁻)) is called a masking matrix.

An important property of the masking matrix diag({hacek over (I)}(i₁ ⁻, i₂ ⁻, . . . , i_(j) ⁻)) is the following: If

diag({hacek over (I)}(i ₁ ⁻ , i ₂ ⁻ , . . . , i _(j) ⁻)) {hacek over (x)} _(t)(n(u))=diag ({hacek over (I)}(i ₁ ⁻ , i ₂ ⁻ , . . . , i _(j) ⁻)) {hacek over (x)} _(T)(n(u))

then

{hacek over (x)} _(t)′(n(u))diag({hacek over (I)}(i ₁ ⁻ , i ₂ ⁻ , . . . , i _(j) ⁻)){hacek over (x)} _(T)(n(u))=2^(dim m(u)−j).

If

diag({hacek over (I)}(i ₁ ⁻ , i ₂ ⁻ , . . . ,i _(j) ⁻)){hacek over (x)} _(t)(n(u))≠diag({hacek over (I)}(i ₁ ⁻ , i ₂ ⁻ , . . . , i _(j) ⁻)){hacek over (x)} _(T)(n(u))

then {hacek over (x)}_(t)′(n(u))diag({hacek over (I)}(i₁ ⁻, i₂ ⁻, . . . , i_(j) ⁻)){hacek over (x)}_(T)(n(u))=0.

Using this property, we combine all such masking matrices that set less than or equal to a selected positive integer J(n(u)) of components of x_(t)(n(u)) equal to zero into the following masking matrix

$\begin{matrix} {{M\left( {n(u)} \right)} = {I + {\sum\limits_{j = 1}^{J{({n{(u)}})}}\; {\sum\limits_{i_{j} = 1}^{\dim \mspace{11mu} {n{(u)}}}\; {\cdots {\sum\limits_{i_{2} = 2}^{i_{3} - 1}\; {\sum\limits_{i_{1} = 1}^{i_{2} - 1}\; {2^{{- 8}j}2^{j}{{diag}\left( {\overset{\Cup}{I}\left( {i_{i}^{-},i_{2}^{-},\ldots \mspace{11mu},i_{j}^{-}} \right)} \right)}}}}}}}}} & (26) \end{matrix}$

where 2^(j) is used to compensate for the factor 2^(−j) in 2^(dim n(u)−j) i in the important property stated above, and 2^(−8j) is an example weight selected to differentiate between different levels of maskings. Some other examples are 2^(−6j), 2^(−7j), 2^(−9j), 10⁻², etc. The weight should be selected to suit the application. M(n(u)) is shown in FIG. 7 and FIG. 9.

Let us denote M(n(u)) by M here for abbreviation. Note that for j=1, . . . , R, we have the following:

-   -   If C_(j)(n(u)){hacek over (x)}_(T)(n(u))≠0, then

D _(j)(n(u)){hacek over (x)} _(T)(n(u))≈D _(j)(n(u))M{hacek over (x)} _(T)(n(u))

C _(j)(n(u)){hacek over (x)} _(T)(n(u))≈C _(j)(n(u))M{hacek over (x)} _(T)(n(u))

Λ_(j)(n(u)){hacek over (x)} _(T)(n(u))≈Λ_(j)(n(u))M{hacek over (x)} _(T)(n(u))

B _(j)(n(u)){hacek over (x)} _(T)(n(u))≈B _(j)(n(u))M{hacek over (x)} _(T)(n(u)).

-   -   If C_(j)(n(u)){hacek over (x)}_(T)(n(u))=0, but

${{{C_{j}\left( {n(u)} \right)}{\sum\limits_{i_{1} = 1}^{\dim \mspace{11mu} {n{(u)}}}\; {{{diag}\left( {\overset{\Cup}{I}\left( i_{1}^{-} \right)} \right)}{{\overset{\Cup}{x}}_{\tau}\left( {n(u)} \right)}}}} \neq 0},$

then

${{D_{j}\left( {n(u)} \right)}{\sum\limits_{i_{1} = 1}^{\dim \mspace{11mu} {n{(u)}}}\; {{{diag}\left( {\overset{\Cup}{I}\left( i_{1}^{-} \right)} \right)}{{\overset{\Cup}{x}}_{\tau}\left( {n(u)} \right)}}}} \approx {{D_{j}\left( {n(u)} \right)}M{{\overset{\Cup}{x}}_{\tau}\left( {n(u)} \right)}}$ ${{C_{j}\left( {n(u)} \right)}{\sum\limits_{i_{1} = 1}^{\dim \mspace{11mu} {n{(u)}}}\; {{{diag}\left( {\overset{\Cup}{I}\left( i_{1}^{-} \right)} \right)}{{\overset{\Cup}{x}}_{\tau}\left( {n(u)} \right)}}}} \approx {{C_{j}\left( {n(u)} \right)}M{{\overset{\Cup}{x}}_{\tau}\left( {n(u)} \right)}}$ ${{A_{j}\left( {n(u)} \right)}{\sum\limits_{i_{1} = 1}^{\dim \mspace{11mu} {n{(u)}}}\; {{{diag}\left( {\overset{\Cup}{I}\left( i_{1}^{-} \right)} \right)}{{\overset{\Cup}{x}}_{\tau}\left( {n(u)} \right)}}}} \approx {{A_{j}\left( {n(u)} \right)}M{{\overset{\Cup}{x}}_{\tau}\left( {n(u)} \right)}}$ ${{B_{j}\left( {n(u)} \right)}{\sum\limits_{i_{1} = 1}^{\dim \mspace{11mu} {n{(u)}}}\; {{{diag}\left( {\overset{\Cup}{I}\left( i_{1}^{-} \right)} \right)}{{\overset{\Cup}{x}}_{\tau}\left( {n(u)} \right)}}}} \approx {{B_{j}\left( {n(u)} \right)}M{{{\overset{\Cup}{x}}_{\tau}\left( {n(u)} \right)}.}}$

-   -   If

${{{C_{j}\left( {n(u)} \right)}{{\overset{\Cup}{x}}_{\tau}\left( {n(u)} \right)}} = 0},{{{C_{j}\left( {n(u)} \right)}{\sum\limits_{i_{1} = 1}^{\dim \mspace{14mu} {n{(u)}}}{{{diag}\left( {\overset{\Cup}{I}\left( i_{1}^{-} \right)} \right)}{{\overset{\Cup}{x}}_{\tau}\left( {n(u)} \right)}}}} = 0},$

but

${{{C_{j}\left( {n(u)} \right)}{\sum\limits_{i_{2} = 1}^{\dim \mspace{14mu} {n{(u)}}}{\sum\limits_{i_{1} = 1}^{i_{2} - 1}{{{diag}\left( {\overset{\Cup}{I}\left( {i_{1}^{-},i_{2}^{-}} \right)} \right)}{{\overset{\Cup}{x}}_{\tau}\left( {n(u)} \right)}}}}} \neq 0},$

then

${{D_{j}\left( {n(u)} \right)}{\sum\limits_{i_{2} = 1}^{\dim \mspace{11mu} {n{(u)}}}{\sum\limits_{i_{1} = 1}^{i_{2} - 1}{{{diag}\left( {\overset{\Cup}{I}\left( {i_{1}^{-},i_{2}^{-}} \right)} \right)}{{\overset{\Cup}{x}}_{\tau}\left( {n(u)} \right)}}}}} \approx {{D_{j}\left( {n(u)} \right)}M{{\overset{\Cup}{x}}_{\tau}\left( {n(u)} \right)}}$ ${{C_{j}\left( {n(u)} \right)}{\sum\limits_{i_{2} = 1}^{\dim \mspace{11mu} {n{(u)}}}{\sum\limits_{i_{1} = 1}^{i_{2} - 1}{{{diag}\left( {\overset{\Cup}{I}\left( {i_{1}^{-},i_{2}^{-}} \right)} \right)}{{\overset{\Cup}{x}}_{\tau}\left( {n(u)} \right)}}}}} \approx {{C_{j}\left( {n(u)} \right)}M{{\overset{\Cup}{x}}_{\tau}\left( {n(u)} \right)}}$ ${{A_{j}\left( {n(u)} \right)}{\sum\limits_{i_{2} = 1}^{\dim \mspace{11mu} {n{(u)}}}{\sum\limits_{i_{1} = 1}^{i_{2} - 1}{{diag}\left( {\overset{\Cup}{I}\left( {i_{1}^{-},i_{2}^{-}} \right)} \right){{\overset{\Cup}{x}}_{\tau}\left( {n(u)} \right)}}}}} \approx {{A_{j}\left( {n(u)} \right)}M{{\overset{\Cup}{x}}_{\tau}\left( {n(u)} \right)}}$ ${{B_{j}\left( {n(u)} \right)}{\sum\limits_{i_{2} = 1}^{\dim \mspace{11mu} {n{(u)}}}{\sum\limits_{i_{1} = 1}^{i_{2} - 1}{{diag}\left( {\overset{\Cup}{I}\left( {i_{1}^{-},i_{2}^{-}} \right)} \right){{\overset{\Cup}{x}}_{\tau}\left( {n(u)} \right)}}}}} \approx {{B_{j}\left( {n(u)} \right)}M{{{\overset{\Cup}{x}}_{\tau}\left( {n(u)} \right)}.}}$

Continuing in this manner, it is seen that D_(j)(n(u)) M{hacek over (x)}_(T)(n(u)), C_(j)(n(u)) M{hacek over (x)}_(T)(n(u)), A_(j)(n(u)) M{hacek over (x)}_(T)(n(u)), B_(j)(n(u)) M{hacek over (x)}_(T)(n(u)) always use the greatest number of uncorrupted, undeviated or unmodified components of x_(T)(n(u)) in estimating d_(Tj)(n(u)), c_(Tj)(n(u)), a_(Tj)(n(u)), b_(Tj)(n(u)), respectively.

Corresponding to {hacek over (x)}_(t)(n), D(n), C(n), A(n), B(n) defined in (7), (8), (9), (10), (11), a general masking matrix is defined as follows:

M(n)=diag[M(n(1)) M(n(2)) . . . M(n(U))]  (27)

where the right side is a matrix with M(n(u)), u=1, 2, . . . , U, as diagonal blocks and zero elsewhere. M(n) is shown in FIG. 9.

If the masking matrix M(n(u)) is used, the symbols a_(T)(n(u)), b_(T)(n(u)), c_(T)(n(u)), d_(T)(n(u)) are defined as follows:

a _(T)(n(u)):=A(n(u)) M(n(u)) {hacek over (x)} _(T)(n(u))   (28)

b _(T)(n(u)):=B(n(u)) M(n(u)) {hacek over (x)} _(T)(n(u))   (29)

c _(T)(n(u)):=C(n(u)) M(n(u)) {hacek over (x)} _(T)(n(u))   (30)

d _(T)(n(u)):=D(n(u)) M(n(u)) {hacek over (x)} _(T)(n(u))   (31)

If the masking matrix M(n) is used, the symbols a_(T)(n), b_(T)(n), c_(T)(n), d_(T)(n) are defined as follows:

$\begin{matrix} \begin{matrix} {{a_{\tau}(n)}:={{A(n)}{M(n)}{{\overset{\Cup}{x}}_{\tau}(n)}}} \\ {= {\sum\limits_{u = 1}^{U}\; {{A\left( {n(u)} \right)}{M\left( {n(u)} \right)}{{\overset{\Cup}{x}}_{\tau}\left( {n(u)} \right)}}}} \end{matrix} & (32) \\ \begin{matrix} {{b_{\tau}(n)}:={{B(n)}{M(n)}{{\overset{\Cup}{x}}_{\tau}(n)}}} \\ {= {\sum\limits_{u = 1}^{U}\; {{B\left( {n(u)} \right)}{M\left( {n(u)} \right)}{{\overset{\Cup}{x}}_{\tau}\left( {n(u)} \right)}}}} \end{matrix} & (33) \\ \begin{matrix} {{c_{\tau}(n)}:={{C(n)}{M(n)}{{\overset{\Cup}{x}}_{\tau}(n)}}} \\ {= {\sum\limits_{u = 1}^{U}\; {{C\left( {n(u)} \right)}{M\left( {n(u)} \right)}{{\overset{\Cup}{x}}_{\tau}\left( {n(u)} \right)}}}} \end{matrix} & (34) \\ \begin{matrix} {{d_{\tau}(n)}:={{D(n)}{M(n)}{{\overset{\Cup}{x}}_{\tau}(n)}}} \\ {= {\sum\limits_{u = 1}^{U}\; {{D\left( {n(u)} \right)}{M\left( {n(u)} \right)}{{\overset{\Cup}{x}}_{\tau}\left( {n(u)} \right)}}}} \end{matrix} & (35) \end{matrix}$

where {hacek over (x)}_(t)(n) is a general orthogonal expansion (GOE) and D(n), C(n), Λ(n) and B(n) are general expansion correlation matrices (GECMs) for PU(n). It follows that

$\begin{matrix} {{a_{\tau}(n)} = {\sum\limits_{u = 1}^{U}\; {a_{\tau}\left( {n(u)} \right)}}} & (36) \\ {{b_{\tau}(n)} = {\sum\limits_{u = 1}^{U}\; {b_{\tau}\left( {n(u)} \right)}}} & (37) \\ {{c_{\tau}(n)} = {\sum\limits_{u = 1}^{U}\; {c_{\tau}\left( {n(u)} \right)}}} & (38) \\ {{d_{\tau}(n)} = {\sum\limits_{u = 1}^{U}\; {d_{\tau}\left( {n(u)} \right)}}} & (39) \end{matrix}$

It is easy to see that d_(T)(n(u))=2a_(T)(n(u))−c_(T)(n(u)), and d_(T)(n)=2a_(T)(n)−c_(T)(n). If c_(Tj)(n)=0, then we set d_(Tj)(n)/c_(Tj)(n)=0. If c_(Tj)(n)≠0, then d_(Tj)(n)/c_(Tj)(n)=2p_(Tj)(n)−1, where p_(Tj)(n) is the probability that the j-th component r_(Tj)(n) of the label r_(T)(n) of x_(T)(n) is +1 based on D(n) and C(n). It follows that

2p _(T)(n)−1 =[d _(T1)(n)/c _(T1)(n) d _(T2)(n)/c _(T2)(n) . . . d _(TR)(n)/c _(TR)(n)]′

is a representative of a probability distribution of the label r_(T)(n) of x_(T)(n).

An estimation means for generating this representative of a probability distribution is shown in FIG. 10: For j=1, 2, . . . , R, if c_(Tj)(n)=0, then set y_(Tj)(n)=0, else set y_(Tj)(n)=d_(Tj)(n)/c_(Tj)(n). The output of the estimation means is y_(T)(n)=[y_(T1)(n) y_(T2)(n) . . . y_(TR)(n)]′, which is a representative 2p_(T)(n)−1 of a probability distribution of the label r_(T)(n) of x_(T)(n).

5.5 Conversion of Probabilities into Ternary Numbers

In a multilayer PAM, a feature vector into layer l is a ternary vector denoted by x_(t) ^(l−1), where t denotes a numbering of the feature vector or a time instant. In this subsection, two methods of converting a representative of a probability distribution, y_(T)(n)=[y_(T1)(n) y_(T2)(n) . . . y_(TR)(n)]′, generated by a PU (processing unit) into a ternary vector are described. Recall y_(Tk)(n)=2p_(Tk)(n)−1, where p_(Tk)(n) is a probability that r_(Tk)(n) is +1. p_(T)(n)=[p_(T1)(n) p_(T2)(n) . . . p_(TR)(n)]′ is an alternative representative of probability distribution of p_(T)(n). y_(T)(n) and p_(T)(n) are related by y_(T)(n)=2p_(T)(n)−I

FIG. 11 shows an example conversion means 13 a for converting y_(T)(n) into a ternary vector x{y_(T)(n)}. Every component y_(Tk)(n) of y_(T)(n) is converted into a one-dimensional ternary vector (i.e., a ternary number) x{y_(Tk)(n)} by the following steps: For k=1, . . . , R, set y_(Tk)(n)=2p_(Tk)(n)−1, and generate a pseudo-random number in accordance with the probability distribution of a random variable v: P(v=1)=p_(Tk)(n) and P(v=−1)=1−p_(Tk)(n), and set x{y_(Tk)(n)} equal to the resultant pseudo-random number. Assemble x{y_(Tk)(n)}, k=1, . . . , R, into a vector x{y_(T)(n)}=[x{y_(T1)(n)} x{y_(T2)(n)} . . . x{y_(TR)(n)}]′. Note that this vector is a bipolar binary vector, which is a ternary vector.

FIG. 12 shows an alternative conversion means 13 b for converting a representative y_(T)(n)=2p_(T)(n)−I of a probability distribution into a ternary vector x{y_(T)(n)}. Assume that each component y_(Tk)(n) of y_(T)(n) is to be converted into a three-dimensional ternary vector. Recall that −1≦y_(Tk)(n)≦1. If y_(Tk)(n) is very close to 0, the probability p_(Tk)(n) is very close to ½ and contains little information about the label r_(Tk)(n). To eliminate it from further processing, the conversion means converts it into x{y_(Tk)(n)}=[0 0 0]. If y_(Tk)(n) is not very close to 0, we convert it into a 3-component ternary vector x{y_(Tk)(n)} as shown in FIG. 12. The output x{y_(T)(n)} of the converter is a 3R-dimensional concatenation of x{y_(Tk)(n)}, k=1, . . . , R. The method of converting a component y_(Tk)(n) of y_(T)(n) into a 3-dimensional ternary vector can easily be generalized to a method of converting y_(Tk)(n) into a ternary vector of any dimensionality.

5.6 Processing Units and Supervised/Unsupervised Learning

An example PU(n) (processing unit on a feature subvector index n) is shown in FIG. 13. The PU comprises expansion means 2, estimation means 54, conversion means 13, adjustment means 9 and storage means 56. A PU has essentially two functions, retrieving the label of a feature subvector from the memory (i.e., ECMs or GECMs) and learning a feature subvector and its label that is either provided from outside the PU (in supervised learning) or generated by itself (in unsupervised learning). In performing retrieval, a feature suvector x_(T)(n) on the FSI n is first expanded into a general orthogonal expansion {hacek over (x)}_(T)(n) by the expansion means 2. {hacek over (x)}_(T)(n) is then processed by the estimation means, using the general expansion correlation matrices, C(n) and D(n), from the storage means 56, into a representative of a probability distribution y_(T)(n) of the label of x_(T)(n). The conversion means converts y_(T)(n) into a ternary vector x{y_(T)(n)}, which is an output of the PU. If a representative of a probability distribution of x_(T)(n) is needed for use outside the PU, y_(T)(n) is also output by the PU. The dashed line in the arrow 55 indicates “output as needed.” y_(T)(n) and x{y_(T)(n)} are the products of retrieval.

C(n) and D(n) are comparable to the synaptic weights in a multilayer perceptron or a cortex. The estimation means and conversion means constitute a processing node, which is comparable to a neuron in a multilayer perceptron or a cortex.

If a label r_(T)(n) of x_(T)(n) from outside the PU is available for learning, supervised learning can be performed by the PU. In the supervised learning mode, the label r_(T)(n) is received through a lever represented by a thick solid line with a solid dot in the position 48 by a general expansion correlation matrix (GECM) adjustment means 9, which receives also {hacek over (x)}_(T)(n) and uses a method of adjusting ECMs such as those depicted in FIG. 5 and FIG. 6 and assembles the resultant ECMs C(n(u)) and D(n(u)), u=1, . . . , U, into general ECMs

C(n)=[C(n(1)) C(n(2)) . . . C(n(U))]

D(n)=[D(n(1)) D(n(2)) . . . D(n(U))]

These C(n) and D(n) are then stored, after a one-numbering delay (or a unit-time delay) 33, in the storage 56, from which they are sent to the estimation means 54. The one-numbering delay is usually a time delay that is long enough for the estimation means to finish using current C(n) and D(n) in generating and outputting y_(T)(n), but short enough for getting the next C(n) and D(n) generated by the adjustment means available for the estimation means to use for processing the next orthogonal expansion or general orthogonal expansion from the expansion means.

Supervised learning means of the PU comprises adjustment means 9 for adjusting at least one GECM (general expansion correlation matrix) by receiving a GOE (general orthogonal expansion) {hacek over (x)}_(T)(n) generated by expansion means 2 and a label r_(T)(n) of x_(T)(n) provided from outside the PAM and replacing said at least one GECM with a weighted sum of said at least one GECM and a product of said label r_(T)(n) and the transpose of said GOE {hacek over (x)}_(T)(n).

If a label r_(T)(n) of x_(T)(n) from outside the PU is unavailable, unsupervised learning can be performed by the PU. In this case, the lever (shown in position 48 in FIG. 13) should be in the position 50. The feature subvector x_(T)(n) is first processed by the expansion means 2, estimation means 54, conversion means 13 as in performing retrieval described above. The resultant ternary vector x{y_(T)(n)} is received, through the lever in position 50, and used by the adjustment means 9 as the label r_(T)(n) of x_(T)(n). The adjustment means 9 uses r_(T)(n)=x{y_(T)(n)} and {hacek over (x)}_(T)(n) to adjust C(n) and D(n) and store the resultant C(n) and D(n) in the storage 56 after a one-numbering delay (or a unit-time delay) 33.

It is sometimes expensive or impossible to provide labels to feature subvectors y_(T)(n) especially for PUs in lower layers of a PAM. If a label r_(T)(n) of x_(T)(n) is not provided from outside the PU(n), unsupervised learning can be performed by the PU. In this case, the lever in position 48 should be switched to the position 50. The ternary vector x{y_(T)(n)} generated by the conversion means in performing retrieval is received and used by the adjustment means 9 as the label r_(T)(n) of x_(T)(n). As in supervised learning described above, the adjustment means 9 uses r_(T)(n) and x_(T)(n) to adjust C(n) and D(n) and store the resultant C(n) and D(n) in the storage 56.

This unsupervised learning method is consistent with the Hebb rule of learning: The synaptic weight between two neurons is increased if the neurons fire at the same time, and the synaptic weight decreases otherwise. Nevertheless, the orthogonal expansion 2 of x_(T)(n(u)), the masking matrix M(n(u)) 11 a, the conversion 13 and the estimation 54 used in this invention are new.

If a feature subvector x_(T)(n) or a slightly different version of it has not been learned by PU(n), and C(n){hacek over (x)}_(T)(n)=0, then y_(T)(n)=0 and p_(T)(n)=(½)I, where I=[1 1 . . . 1]′. The conversion means shown in FIG. 11 converts y_(T)(n) into a purely random label r_(T)(n)=x{y_(T)(n)}, with the probability that x{y_(Tk)(n)}=+1 is equal to ½ for k=1, 2, . . . , R. Once this x_(T)(n) has been learned and stored in C(n) and D(n), if x_(T)(n) is input to PU(n) and to be learned without supervision for the second time, then x{y_(T)(n)}=r_(T)(n) and one more copy of the pair (x_(T)(n), r_(T)(n)) is included in C(n) and D(n). Note that the conversion means 13 b shown in FIG. 12 converts y_(T)(n)=0 into x{y_(Tk)(n)}=0. Hence, this conversion means 13 b in FIG. 12 cannot be used for unsupervised learning.

Assume a feature subvector x_(T)(n) is a noise vector and is given a label in unsupervised learning by PU(n). If the noise vector is not repeatedly fed to the PU, as is usually the case, this noise vector and other noise vectors distruct themselves in C(n) and D(n).

If no learning is to be performed by the PU, the lever represented by a thick solid line with a solid dot is placed in the position 49, through which 0 is sent as the label r_(T)(n) of x_(T)(n) to the adjustment means, which then keeps C(n) and D(n) unchanged or stores the same C(n) and D(n) in the storage 56 after a one-numbering delay (or a unit time delay).

There are three types of PU:

-   -   1. Supervised PU. This type of PU is only capable of performing         supervised learning. In a PU of this type, the position, 50, in         FIG. 13 does not exist. If a label r_(T)(n) of x_(T)(n) from         outside the PU is available and wanted for learning, the lever         represented by a thick solid line with a solid dot is placed in         the position 48, through which r_(T)(n) is sent to the         adjustment means 9. If r_(T)(n) is not available or wanted for         learning, the lever is placed in the position 49, through which         0 is sent to the adjustment means 9. A condition under which         learning is not wanted is given below. It is understood that the         “lever” is simply a symbol used here to explain which “label” to         use by the adjustment means 9.     -   2. Unsupervised PU. This type of PU is only capable of         performing unsupervised learning. In a PU of this type, the         position, 48, in FIG. 13 does not exist. If unsupervised         learning is wanted, then the output x{y_(T)(n)} is used as a         label r_(T)(n) of x_(T)(n), and the lever represented by a thick         solid line with a solid dot is placed in the position 50,         through which r_(T)(n) is sent as to the adjustment means 9. If         no learning is wanted, the lever is placed in the position 49,         through which 0 is sent to the adjustment means 9. A condition         under which learning is not wanted is given below. It is         understood that the “lever” is simply a symbol to explain which         “label” to use by the adjustment means 9.     -   3. Supervised/Unsupervised PU. This type of PU is capable of         performing both supervised and unsupervised learning. In a PU of         this type, all the three positions, 48, 49 and 50, in FIG. 13         coexist. If a label r_(T)(n) of x_(T)(n) from outside the PU is         available and wanted for learning, the lever represented by a         thick solid line with a solid dot is placed in the position 48.         If a label r_(T)(n) of x_(T)(n) from outside the PU is         unavailable, but expected to become available, and y_(T)(n)         generated by the PU's estimation means in its performing         retrieval mode is a zero vector or sufficiently close to it by         some criterion, then the lever is placed in the position 49 and         no learning is performed. This avoids generating and         establishing a randomly selected label in unsupervised learning,         which may turn out to be difficult to “unlearn” through         supervised learning. If a label r_(T)(n) of x_(T)(n) from         outside the PU is unavailable, y_(T)(n) generated by the PU's         estimation means in its performing retrieval is neither a zero         vector nor sufficiently close to it by some criterion, and         unsupervised learning for strengthening learned the knowledge         stored in the GECMs (general expansion correlation matrices) or         ECMs in the PU is wanted, then the lever is placed in the         position 50 and unsupervised learning is performed. A condition         under which learning is not wanted is given below.

A condition under which the lever is placed in the position 49 and no learning is performed is the following: If y_(T)(n) generated by a PU's estimation means in retrieving is a bipolar vector or sufficiently close to a bipolar vector by some criterion, which indicates that the input feature subvector x_(T)(n) is adequately learned, then the lever is placed in the position 49 and no learning is performed. This avoids “saturating” the expansion correlation matrices with one feature subvector and its label.

It is understood that the “lever” and “lever positions” are simply a symbol used here to explain which label to use by the adjustment means 9. In application of the present invention, the “lever” and “lever positions” are implemented usually by software to select among supervised learning, unsupervised learning and no learning. Of course, hardware implementation is also possible.

Note that the learning methods for the three types of PE described above are valid for both batch learning and online learning and jointly or separately, and are suitable for semi-autonomous or autonomous learning. Note also that a PU in a PAM usually has a “receptive field” in the measurement vectors which can be found by tracing the connections in the PAM backforwards from the feature subvectors input to the PU (or the feature subvector index of the PU) to the exogenous feature vectors (or the input terminals) of the PAM, and tracing the transformation, that maps the measurement vectors into exogenous feature vectors, backwards from exogenous feature vectors to measurement vectors. For supervised learning, a feature subvector input to a PU is usually assigned the same label as (or is assigned a translation of) the label of the subvector of the measurement vector that appears in the receptive field of the PU. It is understood that there are other ways to assign a label to a feature subvector for supervised learning.

5.7 Learning to Recognize Rotated, Translated or Scaled Patterns

In this subsection, we describe methods for PUs (processing units) to learn to recognize rotated, translated and scaled patterns. The methods can be modified for PUs to learn to recognize translated and scaled temporal patterns such as speech and music. The methods are valid for both supervised and unsupervised learning. Therefore, labels r_(t)(n) to be referred to may be provided from outside the PAM in supervised learning or generated by the PUs in unsupervised learning for the three types of PU, supervised PUs, unsupervised PUs and supervised/unsupervised PUs described at the end of the last Subsection.

It is assumed in this subsection that feature vectors are arrays of ternary pixels. Other types of feature vector must be converted into arrays of ternary pixels for the methods to be described to apply. For example, an image with 8-bit pixels may be converted by using a pseudo-random number generator to generate a bipolar pulse train for each pixel whose average pulse rate (i.e., the rate of +1 pulse) is proportional to 8-bit light intensity of the pixel. Another way to convert an image with 8-bit pixels is to replace an 8-bit pixel with 3 bipolar 2-bit pixels placed at the same location in considering rotation, translation and scaling. After conversion, at any instant of time, the feature vector is an array of ternary pixels.

Locations of ternary pixels in an array are assumed to be dense relative to the locations of the pixels selected as components of a feature subvector x_(t)(n) input to a PU. We identify the FSI (feature subvector index) n of a feature subvector with the locations of the pixels in x_(t)(n). In other words, the components of n are also the numberings of the locations of the pixels included as components of x_(t)(n).

Consider a PU with an FSI n shown in FIG. 3 and FIG. 17 a. Imagine a thin rubber rectangle with small holes at the locations n of the pixels of the feature subvector with the FSI n. We translate the disk in some directions (e.g., 0, 15, 30, 45, . . . , 330, 345 degrees) (FIG. 17 b) a number of steps (e.g., 0, 1, 2, . . . ), rotate the disk clockwise and counterclockwise a number (e.g., 0, 1, 2, . . . ) of angles (e.g., 0, 5, 10, 15 degrees) at each translation (FIG. 17 c), and expand and compress the rubber disk uniformly for a number of times (e.g., 0, 1, 2, . . . ) at each translation for some percentages (e.g., 0%, 5%, 10%, . . . ) (FIGS. 17 d and 17 e), to obtain other feature subvector indices of the same dimensionality as n. Note that in using the rubber disk to determine an FSI, if a hole in the rubber disk contains more than one pixel in the image, the one nearest to the center of the hole is included in the FSI.

Let Ω(n)={w(i), i=1, . . . , |Ω(n)|} be a set of FSIs w(i) identified with such rotations, translations, and scalings of n including n. Ω(n) is called a rotation/translation/scaling (RTS) suite of n, and |Ω(n)| denotes the number of elements in Ω(n). Note that an RTS suite may contain only rotations, or only translations, or only scalings, or a combination thereof. (Notice the digit 0 in the parentheses (e.g., 0, 1, 2, . . . ) in the last paragraph. It indicates a rotation, a translation, or a scaling that is the feature subvector itself.) As a special case of Ω(n), there is only one element in the set Ω(n) that is n itself. In this special case, |Ω(n)|=1.

Although w(i) is a rotation, translation, or scaling of n, this dependence on n is not indicated in the symbol w(i) for notational simplicity. As n is rotated, translated or scaled into w(i), n(u) as a subvector of n is rotated, translated or scaled into a subvector of w(i). This subvector of w(i) is denoted by n(u,w(i)). The set {n(u,w(i)), i=1, . . . , |Ω(n)|} of such subvectors of w(i), i=1, . . . , |Ω(n)|, is denoted by Ω(n(u)) and called a rotation/translation/scaling (RTS) suite of n(u). Note that |Ω(n(u))|=|Ω(n)|. The set {x_(t)(n(u,w(i))), i=1, . . . , |Ω(n)|}, which is also denoted by {x_(t)(n(u,w)), w ∈ Ω(n)}, is called the rotation/translation/scaling (RTS) suite of x_(t)(n(u)) on Ω(n(u)). In generating and summing orthogonal expansions on an RTS suite Ω(n(u)), elements in the RTS suite of x_(t)(n(u)) on Ω(n(u)) first go through orthogonal expansion 1. The resultant orthogonal expansions {hacek over (x)}_(t)(n(u,w(i))) are then added up to form the sum Σ_(w∈Ω(n)){hacek over (x)}_(t)(n(u,w)) on the RTS suite Ω(n(u)) of n(u) (FIG. 18).

In both the supervised learning and unsupervied learning, the subvectors, x_(t)(n(u,w)), w ∈ Ω(n), on Ω(n(u)) are assigned the label r_(t)(n) of x_(t)(n). ECMs (expansion correlation matrices), C(n(u)), D(n(u)), A(n(u)) and B(n(u)), on Ω(n(u)) are defined by

$\begin{matrix} {{C\left( {n(u)} \right)} = {\Lambda {\sum\limits_{t = 1}^{T}\; {{W_{t}\left( {{n(u)},T} \right)}I{\sum\limits_{\omega \in {\Omega {(n)}}}\; {{\overset{\Cup}{x}}_{t}^{\prime}\left( {n\left( {u,\omega} \right)} \right)}}}}}} & (40) \\ {{D\left( {n(u)} \right)} = {\Lambda {\sum\limits_{t = 1}^{T}\; {{W_{t}\left( {{n(u)},T} \right)}{r_{t}(n)}{\sum\limits_{\omega \in {\Omega {(n)}}}\; {{\overset{\Cup}{x}}_{t}^{\prime}\left( {n\left( {u,\omega} \right)} \right)}}}}}} & (41) \\ {{A\left( {n(u)} \right)} = {\Lambda {\sum\limits_{t = 1}^{T}\; {{W_{t}\left( {{n(u)},T} \right)}\left( {1 + {r_{t}(n)}} \right){\sum\limits_{\omega \in {\Omega {(n)}}}\; {{\overset{\Cup}{x}}_{t}^{\prime}\left( {n\left( {u,\omega} \right)} \right)}}}}}} & (42) \\ {{B\left( {n(u)} \right)} = {\Lambda {\sum\limits_{t = 1}^{T}\; {{W_{t}\left( {{n(u)},T} \right)}\left( {1 - {r_{t}(n)}} \right){\sum\limits_{\omega \in {\Omega {(n)}}}\; {{\overset{\Cup}{x}}_{t}^{\prime}\left( {n\left( {u,\omega} \right)} \right)}}}}}} & (43) \end{matrix}$

Definitions of C(n(u)) and D(n(u)) are shown in FIG. 19.

C(n(u)) and D(n(u)) for the weight matrix W_(t)(n(u),T)=λ^(T−t)I can be adjusted to learn a pair (x_(t),r_(t)(n)), where λ is a forgetting factor, and Λ is a scaling constant. If r_(T)(n)≠0, D(n(u)) and C(n(u)) are replaced respectively with λD(n(u))+Λr_(T)(n) Σ_(w∈Ω(n)) {hacek over (x)}_(t)′(n(u,w)) and λC(n(u))+ΛΣ_(wCΩ(n)){hacek over (x)}_(t)′(n(u,w)), which are each a weighted sum of a ECM and r_(T)(n)Σ_(w∈Ω(n)){hacek over (x)}_(t)′(n(u,w)) or Σ_(w∈Ω(n)){hacek over (x)}_(t)′(n(u,w)). If r_(T)(n)=0, then D(n(u)) and C(n(u)) are unchanged. Note that W_(t)(n(u),T) is a diagonal matrix, and C(n(u)) is a row vector here. This adjustment of the ECMs is shown in FIG. 20.

If W_(t)(n(u),T)=I/√{square root over (T)}, the ECMs are adjusted as follows: If r_(T)(n)≠0, D(n(u)) and C(n(u)) are replaced with (√{square root over (T)}D(n(u))+Λr_(T)(n){hacek over (x)}_(t)′(n(u)))/√{square root over (T+1)} and (√{square root over (T)}C(n(u))+Λ{hacek over (x)}_(t)′(n(u)))/√{square root over (T+1)}, which are each a weighted sum of a ECM and r_(T)(n)Σ_(w∈Ω(n)){hacek over (x)}_(l)′(n(u,w)) or Σ_(w∈Ω(n)){hacek over (x)}_(l)′(n(u,w)), and √{square root over (T)} is replaced with √{square root over (T+1)}. If r_(T)(n)=0, then D(n(u)) and C(n(u)) are unchanged. Note that W_(t)(n(u),T) is a diagonal matrix, and C(n(u)) is a row vector here. This adjustment of the ECMs is shown in FIG. 21.

Sums Σ_(w∈Ω(n)){hacek over (x)}_(t)(n(u,w)) of orthogonal expansions (OEs), and ECMs, D(n(u)), C(n(u)), A(n(u)), B(n(u)), u=1, . . . , U, are respectively assembled into a general orthogonal expansion (GOE) {hacek over (x)}_(t)(n) and general expansion correlation matrices (GECMs), D(n), C(n), A(n), B(n), for PU(n) (the PU on the feature vector n) as follows:

{hacek over (x)} _(t)(n,Ω)=[Σ_(w∈Ω(n)) {hacek over (x)} _(t)′(n(1,w)) . . . Σ_(w∈Ω(n)) {hacek over (x)} _(t)′(n(2,w))]  (44)

D(n)=[D(n(1)) D(n(2)) . . . D(n(U))]  (45)

C(n)=[C(n(1)) C(n(2)) . . . C(n(U))]  (46)

A(n)=[A(n(1)) A(n(2)) . . . A(n(U))]  (47)

B(n)=[B(n(1)) B(n(2)) . . . B(n(U))]  (48)

where these definitions of {hacek over (x)}_(t)(n,Ω), D(n) and C(n) are shown in FIG. 22.

How a GOE (general orthogonal expansion) {hacek over (x)}_(t)(n,Ω) on an RTS suite Ω(n), is generated is shown in FIG. 23.

5.8 Processing Units for Recognizing Rotated, Translated and Scaled Patterns

An example PE (processing unit) that is capable of recognizing rotated, translated and scaled images of causes (e.g., objects) is given in FIG. 24. Notice that FIG. 24 is essentially the same as FIG. 13 except that the input feature subvector x_(T)(n), box 2 (expansion means for generating GOEs on n) and box 9 (adjustment means for adjusting GECMs) in FIG. 13 are respectively replaced with x_(T), box 18 (expansion means for generating GOEs on n and GOEs on Ω(n)) and box 9 (adjustment means for adjusting GECMs on Ω(n)) in FIG. 24.

The feature vector x_(T)that is input to PE in FIG. 24 is first used 18 to generate the GOE (general orthogonal expansion) {hacek over (x)}_(T)(n) defined in (7) (FIG. 8) and GOE {hacek over (x)}_(t)(n,Ω) on the RTS suite Ω(n) defined in (44) (FIG. 22). The GOE {hacek over (x)}_(T)(n) is then sent to the estimation means 54, and the GOE {hacek over (x)}_(t)(n,Ω) on Ω(n) is sent to the adjustment means 9.

Responsible to {hacek over (x)}_(T)(n,Ω) and the label r_(T)(n), which is provided from outside the PU in supervised learning or generated by the conversion means 13, the adjustment means 9 adjusts GECMs (general expansion correlation matrices) D(n) and C(n) on Ω(n), which are defined in (45) and (46) (FIG. 22). The adjustment is performed by replacing D(n(u)) with a weighted sum of D(n(u)) and r_(T)(n)Σ_(w∈Ω(n)){hacek over (x)}_(T)′(n(u,w)) and replacing C(n(u)) with a weighted sum of C(n(u)) and IΣ_(w∈Ω(n)){hacek over (x)}_(T)′(n(u,w)). If W_(t)(n(u),T)=w_(t)(n(u),T) I, all rows of C(n(u)) are the same and only one row is needed to represent C(n(u)), and IΣ_(w∈Ω(n)){hacek over (x)}_(T)′(n(u,w)) used for adjusting C(n (u)) can be replaced with Σ_(w∈Ω(n)){hacek over (x)}_(T)′(n(u,w)). Two examples of the adjustment means are given in FIG. 20 and FIG. 21.

Other than the foregoing differences between the PUs in FIG. 13 and FIG. 24, these PUs' estimation means 54, conversion means 13, supervised learning means 48, 9, unsupervised learning means 49, 9, storage 56 of the GECM [C′(n) D′(n)] function in much the same way.

If more capability of recognizing rotated, translated and scaled images is required of the PU, an additional RTS suite, Ω₁(n), is used. Instead of generating the GOE {hacek over (x)}_(T)(n), another GOE {hacek over (x)}_(t)(n,Ω₁) on the RTS suite Ω₁(n) is generated and sent to the estimation means 54. In generating a representative y_(T)(n) of a probability distribution, {hacek over (x)}_(t)(n,Ω₁) is used here in box 54. Such a PU is shown in FIG. 25.

There are also three types of PU, supervised PUs, unsupervised PUs and supervised/unsupervised PUs as discussed at the end of the Subsection on “Processing Units and Supervised/Unsupervised Learning.”

5.9 Multilayer and Recurrent Networks

An embodiment of the present invention comprises at least one layer of PUs, which are discussed in the Subsection on “Processing Units and Supervised/Unsupervised Learning” and the Subsection on “Processing Units for Recognizing Rotated, Translated and Scaled Images.” A typical layer, layer l, is shown in FIG. 14. A feature vector input to layer l comprises an exogenous feature vector x_(T) ⁰ input to the network. In this Subsection, l is used in superscripts to emphasize dependency on layer numberings such as l−1 and l.

There are at least one PU in layer l (5 in FIG. 14). The PUs in layer l have FSIs (feature subvector indices) denoted by 1^(l), 2^(l), . . . , N^(l). Upon receiving a feature vector x_(T) ^(l−1) by layer l, the feature subvectors, x_(T) ^(l−1) (1^(l)), x_(T) ^(l−1) (2^(l)), . . . , x_(T) ^(l−1) (N^(l)), are formed and processed by the PUs (15 in FIG. 14), PU(1^(l)), PU(2^(l)), . . . , PU(N^(l)) to generate x{y_(T) ^(l)(1^(l))}, x{y_(T) ^(l)(2^(l))}, . . . , x{y_(T) ^(l)(N^(l))}, respectively. These ternary vectors are then assembled (42 in FIG. 14) into the output vector x{y_(T) ^(l)} of layer l. If needed, y_(T) ^(l)(1^(l)), y_(T) ^(l)(2^(l)), . . . , y_(T) ^(l)(N^(l)), are also assembled and output from layer l.

If an embodiment of the present invention comprises a plurality of layers (5 in FIG. 14) with only feedforward connections and no feedback connections as shown in FIG. 15, the embodiment is called a hierarchical probabilistic associative memory (HPAM). The feature vector input to layer l=1 is the exogenous feature vector x_(T) ⁰ input to the embodiment. If l>1, the components of a feature vector x_(T) ^(l−1) input to layer l are components of ternary vectors, x{y_(T) ^(l−1)(1^(l−1))}, x{y_(T) ^(l−1)(2^(l−1))}, . . . , x{y_(T) ^(l−1)(N^(l−1))}, generated by PU(1^(l−1)), PU(2^(l−1)), . . . , PU(N^(l−1)) in layer l−1.

If the above embodiment further comprises feedback connections, it is called a temporal hierarchical probabilistic associative memory (THPAM). An example THPAM is shown in FIG. 16. Two types of feedback connections are shown, namely same-layer feedback connections (371, 372, 373, 374 in FIG. 16) and 1-layer feedback connections (352, 353, 354 in FIG. 16). The components of a feature vector x_(T) ^(l−1) input to layer l at time (or with numbering) T comprise components of ternary vectors generated by PUs in layer l−1 and generated at a previous time (or for a feature vector with a lower numbering) by PUs in the same layer l or PUs in higher-ordered layers with layer numberings l+k for some positive integers k. For example, the components of the feature vector x_(T) ² input to layer 3 in the example THPAM are components of x{y_(T) ²} generated by PUs in layer 2 and components of x{y_(T−1) ⁴} and x{y_(T−1) ³}, which are generated by PUs in layer 4 and layer 3 for the exogenous feature vector input to the THPAM at time T−1. Note that the small boxes 33 enclosing 1 in FIG. 16 are delay devices.

Once an exogenous feature vector is received by an HPAM or THPAM, the PUs perform functions of retrieving and/or learning from layer to layer starting with layer 1, the lowest-ordered layer. After the PUs in the highest-ordered layer, layer L, complete performing their functions, the HPAM or THPAM is said to have completed one round of retrievings and/or learnings (or memory adjustments).

It is understood that an HPAM may further comprises feedforward connections with delay devices, and that the delay devices in a THPAM or an HPAM may effect delays of more than one unit of time (or one numbering) or even different lengths.

5.10 Processing Units for Recognizing Extensively Translated Images

Assume that FSIs, 1^(l), 2^(l), . . . , N^(l), in layer l are translations of one another, and hence so are their subvectors 1^(l)(u), 2^(l)(u), . . . , N^(l)(u), for each u=1, . . . , U^(l), where U^(l) denotes the number of FSIs in layer l. Recall that the GOE (general orthogonal expansion) {hacek over (x)}_(T) ^(l−1)(n) defined in (7) (FIG. 8) and the GOE {hacek over (x)}_(t) ^(l 1)(n,Ω) on the RTS suite Ω(n) are defined in (44) (FIG. 22). The general orthogonal expansion on n^(l) is

{hacek over (x)} _(l) ^(l−1)(n ^(l))=[{hacek over (x)} _(t) ^(l−1)′(n ^(l))) {hacek over (x)} _(t) ^(l−1)′(n ^(l)(2)) . . . {hacek over (x)} _(t) ^(l−1)′(n ^(l)(U))]′

and the general orthogonal expansion on Ω(n^(l)) is

{hacek over (x)} _(t) ^(l−1)′(n ^(l),Ω)=[Σ_(w∈Ω(n) _(l) ₎ {hacek over (x)} _(t) ^(l−1)′(n ^(l)(1,w)) . . . Σ_(w∈Ω(n)) {hacek over (x)} _(t) ^(l−1)′(n ^(l)(2,w))]

Here, l is used in superscripts to emphasize dependency on layer l−1 or layer l.

To enable recognition of an object or cause in an image translated across the receptive field of a PAM, GECMs (general expansion correlation matrices) on all RTS suites Ω(n^(l)) in layer l are summed up to be used in every PU in the layer. Such sums are called common GECMs (CGECMs) on all RTS suites Ω(n^(l)) in layer l. More specifically, the CGECMs on all RTS suites Ω(n^(l)) in layer l are defined by (FIG. 26):

$\begin{matrix} {D^{l} = {\sum\limits_{n^{l} = 1^{l}}^{N^{l}}\; {D^{l}\left( n^{l} \right)}}} & (49) \\ {C^{l} = {\sum\limits_{n^{l} = 1^{l}}^{N^{l}}\; {C^{l}\left( n^{l} \right)}}} & (50) \\ {A^{l} = {\sum\limits_{n^{l} = 1^{l}}^{N^{l}}\; {A^{l}\left( n^{l} \right)}}} & (51) \\ {B^{l} = {\sum\limits_{n^{l} = 1^{l}}^{N^{l}}\; {B^{l}\left( n^{l} \right)}}} & (52) \end{matrix}$

where D^(l)(n^(l)), C^(l)(n^(l)), A^(l)(n^(l)), B^(l (n) ^(l)) are GECMs on Ω(n^(l)) defined in (45), (46), (47), (48), and shown in FIG. 22.

A PU (processing unit) on FSI n^(l) that can recognize rotated, translated and scaled objects or causes and can recognize objects and causes translated across its receptive field is shown in FIG. 27. The PU is the same as that shown in FIG. 24 except that the GECMs, D^(l)(n^(l)) and C^(l)(n^(l)), generated by the adjustment means 9 are output from the PU, and the CGECMs, D^(l) and C^(l), defined in (49) and (50), are received from outside the PU and delayed for one unit of time (or one numbering) 33 before stored 56 and used by the estimation means 54.

To acquire more capability to recognize rotated, translated and scaled objects or causes in images, a GOE (general orthogonal expansion) {hacek over (x)}_(t) ^(l−1)(n^(l),Ω₁) on a RTS suite Ω₁(n^(l)), which may be different from the RTS suite Ω(n^(l)), is generated and used by the estimation means in PU(n^(l)). Such a PU is shown in FIG. 28.

A layer of PUs mentioned above is shown in FIG. 29. The GECMs D^(l)(n^(l)), C^(l)(n^(l)), A^(l)(n^(l)), B^(l)(n^(l)) are GECMs on Ω(n^(l)) defined in (45), (46), (47), (48) (FIG. 22). D^(l)(n^(l)) and C^(l)(n^(l)), n^(l)=1^(l), 2^(l), . . . , N^(l), are summed up 64 to form D^(l) and C^(l), which are distributed to every PU(n^(l)), n^(l)=1^(l), 2^(l), . . . , N^(l).

An example hierarchical probabilistic associative memorys (HPAMs) and an example temporal hierarchical probabilistic associative memorys (THPAMs) with PEs described above are shown in FIG. 15 and FIG. 16, respectively.

It is understood that an HPAM may further comprises feedforward connections with delay devices, and that the delay devices in a THPAM or an HPAM may effect delays of more than one unit of time (or one numbering) or even different lengths.

5.11 Pulse Trains for Each Exogenous Feature Vector

Recall that a ternary vector x{y_(T)(n)} output from a processing unit, PU(n), is obtained by converting a representative y_(T)(n) of a probability distribution of a label r_(T)(n) of a feature subvector x_(T)(n). If conversion means in PU(n) uses a pseudo-random number generator as shown in FIG. 11 and if some components of y_(T)(n) are greater than −1 and less than 1, then the corresponding components of x{y_(T)(n)} contain uncertainty, which reflects probabilistic information contained in y_(T)(n). When a PU, say PU(m), receives a feature subvector with such components with uncertainty, it uses masking matrices or general masking matrices to suppress or “filter out” those components that make the received feature subvector inconsistent with those stored in its ECMs or GECMs in trying to find a match between the received feature subvector and feature subvectors stored in those ECMs or GECMs. (Masking matrices are described in the Subsections on “Masking Matrices.”)

To give PU(m) more pseudo-random outcomes or realizations of x{y_(T)(n)} to try, it is sometimes desirable or necessary to use said conversion means to generate a sequence of ternary vectors denoted by x{y_(T)(n,j)},j =1, 2, . . . , J, for the same exogenous feature vector x_(T) ⁰. This increases the chance for the estimation means in PUs using these ternary components to find a match in its ECMs or GECMs. Here J is a preselected positive integer. If all PUs in a PAM generate J ternary vectors for an exogenous feature vector x_(T) ⁰, there are J possibly different feature subvectors input to each PU, that is not in layer 1, for the exogenous feature vector.

With the exogenous feature vector x_(T) ⁰, labels r_(T)(n) provided from outside the PAM, and delayed feedbacks x{y_(T−1)(n)} held constant, each PU(n) in the PAM generates J ternary vectors x{y_(T)(n,j)}, j=1, 2, . . . , J, during the time period between the time instances two consecutive exogenous feature vectors x_(T) ⁰ and x_(T+1) ⁰ are received by the PAM. If said time period is called 1 unit of time, each of the J ternary vectors x{y_(T)(n,j)}, j=1, 2, . . . , J, is generated in 1/J unit of time. In x{y_(T)(n,j)}, j=1, 2, . . . , J, the k-th components of each ternary vector in this sequence forms a ternary pulse train for k=1, 2, . . . , R.

5.11.1 GECMs with Multiple/Group Adjustments on an FSI n for Each Exogenous Feature Subvector

For an exogenous feature vector x_(T) ⁰, let the J feature subvectors input to PU(n) be denoted by x_(T)(n,j), j=1, 2, . . . , J, and their GOEs (general orthogonal expansions) be denoted by {hacek over (x)}_(T)(n,j), j=1, 2, . . . , J. Note that

{hacek over (x)} _(T)(n,j)=[{hacek over (x)} _(T)(n(1),j) {hacek over (x)} _(T)(n(2),j) . . . {hacek over (x)} _(T)(n(U),j)]

By supervised learning, the GECMs, C(n) and D(n), on an FSI n with J adjustments for each exogenous feature subvector x_(t) ⁰, t=1, . . . , T, are the following:

$\begin{matrix} {{C(n)} = {\Lambda {\sum\limits_{t = 1}^{T}\; {{W_{t}\left( {n,T} \right)}I\frac{1}{J}{\sum\limits_{j = 1}^{J}\; {{\overset{\Cup}{x}}_{t}^{\prime}\left( {n,j} \right)}}}}}} & (53) \\ {{D(n)} = {\Lambda {\sum\limits_{t = 1}^{T}\; {{W_{t}\left( {n,T} \right)}{r_{t}(n)}\frac{1}{J}{\sum\limits_{j = 1}^{J}\; {{\overset{\Cup}{x}}_{t}^{\prime}\left( {n,j} \right)}}}}}} & (54) \end{matrix}$

where the label r_(T)(n) is provided from outside the PAM (FIG. 31).

There are two ways to adjust C(n(u)) and D(n(u)) in supervised learning. First, they are adjusted for each {hacek over (x)}_(t)(n,j), and hence are adjusted J times for each exogenous feature vector x_(t) ⁰. Second, C(n(u)) and D(n(u)) are adjusted only once, after {hacek over (x)}_(t)(n,j), j=1, . . . , J, are all received and

$\frac{1}{J}{\sum\limits_{j = 1}^{J}\; {{\overset{\Cup}{x}}_{\tau}\left( {n,j} \right)}}$

is obtained. In the first way, {hacek over (x)}_(t)(n,j), j=1, . . . , J, input to PU(n) have been generated with different GECMs by other PUs. In the second way, all PUs in the PAM keep their GECMs unchanged for j=1, . . . , J. The first way involves multiple adjustments of GECMs, and the second one group adjustment of the same. To faciliate multiple adjustments, we need a delay device in each PU that holds the GECMs for 1/J unit of time, before sends them to the storage (FIG. 31).

A example of the second way, which involves a group adjustment is the following: If W_(t)(n, T)=λ^(T−t)I, the GECMs are adjusted as follows: If r_(T)(n)≠0, D(n) and C(n) are replaced respectively with

${\lambda \; {D(n)}} + {\Lambda \; {r_{\tau}(n)}\frac{1}{J}{\sum\limits_{j = 1}^{J}{{\overset{˘}{x}}_{t}^{\prime}\left( {n,j} \right)}}}$

and

${{\lambda \; {C\left( {n(u)} \right)}} + {\Lambda \frac{1}{J}{\sum\limits_{j = 1}^{J}{{\overset{˘}{x}}_{t}^{\prime}\left( {n,j} \right)}}}},$

which are each a weighted sum of a GECM and

${r_{\tau}(n)}\frac{1}{J}{\sum\limits_{j = 1}^{J}{{\overset{˘}{x}}_{t}^{\prime}\left( {n,j} \right)}}$

or

$\frac{1}{J}{\sum\limits_{j = 1}^{J}{{{\overset{˘}{x}}_{t}^{\prime}\left( {n,j} \right)}.}}$

If r_(T)(n)=0, then D(n) and C(n) are unchanged. Note that W_(t)(n,T) is a diagonal matrix, and C(n) is a row vector here. Adjustment of ECMs that are block columns of D(n) and C(n) is shown in FIG. 32.

Another example of the second way is the following: If W_(l)(n,T)=I/√{square root over (T)}, the GECMs are adjusted as follows: If r_(T)(n)≠0, D(n) and C(n) are replaced respectively with

$\left( {{\sqrt{T}{D(n)}} + {\Lambda \; {r_{\tau}(n)}\frac{1}{J}{\sum\limits_{j = 1}^{J}\; {{\overset{\Cup}{x}}_{t}^{\prime}\left( {n,j} \right)}}}} \right)/\sqrt{T + 1}$

and

${\left( {{\sqrt{T}{C(n)}} + {\Lambda \frac{1}{J}{\sum\limits_{j = 1}^{J}\; {{\overset{\Cup}{x}}_{t}^{\prime}\left( {n,j} \right)}}}} \right)/\sqrt{T + 1}},$

which are each a weighted sum of a GECM and

${r_{\tau}(n)}\frac{1}{J}{\sum\limits_{j = 1}^{J}{{\overset{˘}{x}}_{t}^{\prime}\left( {n,j} \right)}}$

or

${\frac{1}{J}{\sum\limits_{j = 1}^{J}{{\overset{˘}{x}}_{t}^{\prime}\left( {n,j} \right)}}},$

and √{square root over (T)} is replaced with √{square root over (T+1)}. If r_(T)(n)=0, then D(n(u)) and C(n(u)) are unchanged and T is not replaced with T+1. Note that W_(t)(n,T) is a diagonal matrix, and C(n) is a row vector here. Note here that 1/J above is an example weight, which can be replaced by a weight more suitable for an application.

In unsupervised learning by PU(n), the y_(t)(n,j*) that is closest to a bipolar vector among y_(t)(n,j), j=1, 2, . . . , J, is first determined with respect to a certain criterion, and the corresponding x{y_(t)(n,j*)} is used as the label of its GOE {hacek over (x)}_(t)(n,j*) to adjust C(n) and D(n), which are defined as follows (FIG. 33):

$\begin{matrix} {{C(n)} = {\Lambda {\sum\limits_{t = 1}^{T}{{W_{t}\left( {n,T} \right)}I{{\overset{˘}{x}}_{t}^{\prime}\left( {n,j^{*}} \right)}}}}} & (55) \\ {{D(n)} = {\Lambda {\sum\limits_{t = 1}^{T}{{W_{t}\left( {n,T} \right)}x\left\{ {y\; {t\left( {n,j^{*}} \right)}} \right\} {{\overset{˘}{x}}_{t}^{\prime}\left( {n,j^{*}} \right)}}}}} & (56) \end{matrix}$

where an example of said certain criterion is the following:

$\begin{matrix} {j_{t}^{*} = {\arg \mspace{14mu} {\min\limits_{j \in {\{{1,\mspace{14mu} \ldots \mspace{14mu},J}\}}}{\sum\limits_{k = 1}^{R}{{p_{tk}\left( {n,j} \right)}\left( {1 - {p_{tk}\left( {n,j} \right)}} \right)}}}}} & (57) \end{matrix}$

For example, if W_(t)(n,T)=λ^(T−t)I, the GECMs are adjusted as follows (FIG. 34): The probabilities p_(Tk)(n,j), k=1, . . . , K, j=1, . . . , J, are first obtained. Then j_(t)′* is determined by (57) or another criterion. D(n) and C(n) are replaced respectively with λD(n)+Λx{y_(T)(n,j*)}{hacek over (x)}_(T)′(n,j*) and λC(n(u))+Λ{hacek over (x)}_(T)′(n,j*), which are each a weighted sum of a GECM and x{y_(T)(n,j*)}{hacek over (x)}_(T)′(n, j*) or {hacek over (x)}_(T)(n,j*). Note that W_(t)(n,T) is a diagonal matrix, and C(n) is a row vector here.

If W_(t)(n,T)=I/√{square root over (T)}, the GECMs are adjusted as follows: If r_(T)(n)≠0, D(n) and C(n) are replaced respectively with (√{square root over (T)}D(n)+Λx{y_(T)(n,j*)}{hacek over (x)}_(T)′(n,j*))/√{square root over (T+1 )}and (√{square root over (T)}C(n(u))+Λ{hacek over (x)}_(T)′(n,j*)/√{square root over (T+1)}, which are each a weighted sum of a GECM and x{y_(T)(n,j*)}{hacek over (x)}_(T)′(n,j*) or {hacek over (x)}_(T)′(n,j*), and √{square root over (T)} is replaced with √{square root over (T+1)}. Note that W_(t)(n,T) is a diagonal matrix, and C(n) is a row vector here.

5.11.2 GECMs on an RTS Suite Ω(n) with Group/Multiple Adjustments for One Exogenous Feature Subvector

Let the J GOEs (general orthogonal expansions) on an RTS Suite Ω(n) be denoted by {hacek over (x)}_(t)(n,Ω,j), j=1, 2, . . . , J. By supervised learning, the GECMs, C(n) and D(n), on an RTS suite Ω(n) with J adjustments for each exogenous feature subvector x_(t) ⁰, t=1, . . . , T, are the following (FIG. 35):

$\begin{matrix} {{C(n)} = {\Lambda {\sum\limits_{t = 1}^{T}{{W_{t}\left( {n,T} \right)}I\frac{1}{J}{\sum\limits_{j = 1}^{J}{{\overset{˘}{x}}_{t}^{\prime}\left( {n,\Omega,j} \right)}}}}}} & (58) \\ {{D(n)} = {\Lambda {\sum\limits_{t = 1}^{T}{{W_{t}\left( {n,T} \right)}{r_{t}(n)}\frac{1}{J}{\sum\limits_{j = 1}^{J}{{\overset{˘}{x}}_{t}^{\prime}\left( {n,\Omega,j} \right)}}}}}} & (59) \end{matrix}$

where r_(t)(n)≠0. Note that in supervised learning, the label r_(T)(n) is provided from outside the PAM. C(n) and D(n) can be adjusted by multiple adjustments or a group adjustment as GECMs on an FSI n can be. Two examples for a group adjustment are given below.

For example, if W_(t)(n,T)=λ^(T−t)I, the GECMs are adjusted as follows (FIG. 36): If r_(T)(n)≠0, D(n) and C(n) are replaced respectively with

${\lambda \; {D(n)}} + {\Lambda \; {r_{\tau}(n)}\frac{1}{J}{\sum\limits_{j = 1}^{J}{{\overset{˘}{x}}_{t}^{\prime}\left( {n,\Omega,j} \right)}}}$

and

${{\lambda \; {C\left( {n(u)} \right)}} + {\Lambda \; \frac{1}{J}{\sum\limits_{j = 1}^{J}{{\overset{˘}{x}}_{t}^{\prime}\left( {n,\Omega,j} \right)}}}},$

which are each a weighted sum of a GECM and

${r_{\tau}(n)}\frac{1}{J}{\sum\limits_{j = 1}^{J}{{\overset{˘}{x}}_{t}^{\prime}\left( {n,\Omega,j} \right)}}$

or

$\frac{1}{J}{\sum\limits_{j = 1}^{J}{{{\overset{˘}{x}}_{t}^{\prime}\left( {n,\Omega,j} \right)}.}}$

If r_(T)(n)=0, then D(n(u)) and C(n(u)) are unchanged. Note that W_(t)(n,T) is a diagonal matrix, and C(n) is a row vector here.

If W_(t)(n,T)=I/√{square root over (T)}, the GECMs are adjusted as follows: If r_(T)(n)≠0, D(n) and C(n) are replaced respectively with

$\left( {{\sqrt{T}{D(n)}} + {\Lambda \; {r_{\tau}(n)}\frac{1}{J}{\sum\limits_{j = 1}^{J}{{{\overset{˘}{x}}_{t}^{\prime}\left( {n,\Omega,j} \right)}/\sqrt{T + 1}}}}} \right.$

and

$\left( {{{\sqrt{T}{C(n)}} + {\Lambda \frac{1}{J}{\sum\limits_{j = 1}^{J}{{{\overset{˘}{x}}_{t}^{\prime}\left( {n,\Omega,j} \right)}/\sqrt{T + 1}}}}},} \right.$

which are each a weighted sum of a GECM and

${r_{\tau}(n)}\frac{1}{J}{\sum\limits_{j = 1}^{J}{{\overset{\Cup}{x}}_{t}^{\prime}\left( {n,\Omega,j} \right)}}$

or

${\frac{1}{J}{\sum\limits_{j = 1}^{J}{{\overset{\Cup}{x}}_{t}^{\prime}\left( {n,\Omega,j} \right)}}},$

and √{square root over (T)} is replaced with √{square root over (T+1)}. If r_(t)(n)=0, D(n(u)) and C(n(u)) are unchanged and the numbering T is not incremented by 1. Note that W_(t)(n,T) is a diagonal matrix, and C(n) is a row vector here. Note here that 1/J above is an example weight, which can be replaced by a weight more suitable for an application.

By unsupervised learning, the GECMs, C(n) and D(n), on an RTS suite Ω(n) with multiple adjustments for each exogenous feature subvector x_(t) ⁰, t=1, . . . , T, are the following (FIG. 37):

$\begin{matrix} {{C(n)} = {\Lambda {\sum\limits_{t = 1}^{T}{{W_{t}\left( {n,T} \right)}I{{\overset{\Cup}{x}}_{t}^{\prime}\left( {n,\Omega,j_{t}^{*}} \right)}}}}} & (60) \\ {{D(n)} = {\Lambda {\sum\limits_{t = 1}^{T}{{W_{t}\left( {n,T} \right)}x\left\{ {y_{t}\left( {n,j_{t}^{*}} \right)} \right\} {{\overset{\Cup}{x}}_{t}^{\prime}\left( {n,\Omega,j_{t}^{*}} \right)}}}}} & (61) \end{matrix}$

where y_(t)(n,j*) is closest to a bipolar vector among y_(t)(n,j), j=1, 2, . . . , J with respect to a certain criterion, say,

$j_{t}^{*} = {\arg \; {\min\limits_{j}{\sum\limits_{k = 1}^{R}{{y_{tk}\left( {n,j} \right)}\left( {1 - {y_{tk}\left( {n,j} \right)}} \right)}}}}$

For example, if W_(t)(n,T)=λ^(T−t)I, the GECMs are adjusted without supervision as follows: If r_(T)(n)≠0, D(n) and C(n) are replaced respectively with λD(n)+Λx{y_(T)(n,j_(T)*)}{hacek over (x)}_(t)′(n,Ω,j_(t)*) and λC(n(u))+Λ{hacek over (x)}_(t)′(n,Ω,j_(t)*), which are each a weighted sum of a GECM and x{y_(T)(n,j*)}{hacek over (x)}_(t)′(n,Ω,j_(t)*) or {hacek over (x)}_(t)′(n,Ω,j_(t)*). If r_(T)(n)=0, then D(n) and C(n) are unchanged. Note that W_(t)(n,T) is a diagonal matrix, and C(n) is a row vector here. This example is shown in FIG. 38

If W_(t)(n,T)=I/√{square root over (T)}, the GECMs are adjusted without supervision as follows: If r_(T)(n)≠0, D(n) and C(n) are replaced respectively with (√{square root over (T)}D(n)+Λx{y_(T)(n,j_(T)*)}{hacek over (x)}_(t)′(n,Ω,j_(t)*)/√{square root over (T+1)} and (√{square root over (T)}C(n(u))+Λ{hacek over (x)}_(t)′(n,Ω,j_(t)*)/√{square root over (T+1)}, which are each a weighted sum of a GECM and x{y_(T)(n,j_(T)*)}{hacek over (x)}_(t)′(n,Ω,j_(t)*) or {hacek over (x)}_(t)′(n,Ω,j_(t)*), and √{square root over (T)} is replaced with √{square root over (T+1)}. If r_(T)(n)=0, then D(n) and C(n) are unchanged, and T is not increment by 1. Note that W_(t)(n,T) is a diagonal matrix, and C(n) is a row vector here.

The adjustment of GECMs, C(n) and D(n), on an RTS suite Ω(n) described above is performed by adjustment means 9 as shown in FIG. 24 and FIG. 25.

The above descriptions and formulas can be easily extended to CGECMs (common general expansion correlation matrices) defined in FIG. 26. To avoid making this description too long, the descriptions and formulas for CGECMs will not be given here. Those skilled in the art should have no difficulty with the extension.

5.12 Combination of Probability Distributions

Let m₁, m₂, . . . , m_(η) be FSIs (feature subvector indices), which may come from a single layer or from different layers of PUs, but the labels, r_(T)(m₁), r_(T)(m₂), . . . , r_(T)(m_(η)), of the feature vectors, x_(T)(m₁), x_(T)(m₂), . . . , x_(T)(m_(η)), on these FSIs are equal. Recall that p_(Tk)(n) denotes the probability that the k-th component r_(Tk) of the label r_(T) of x_(T)(n) is equal to 1, and that p_(Tk)(n)=(y_(Tk)(n)+1)/2, where y_(Tk)(n) is generated by the estimation means in the PU on n.

In this subsection, we show how to combine the probabilities, p_(Tk)(m_(i)), i=1, 2, . . . , j, into an estimate {circumflex over (P)}_(Tk) of P_(Tk)=P(r_(Tk)=1|d_(Tk),c_(Tk)) for each k=1, 2, . . . , R, where P(r_(Tk)=1|d_(Tk), c_(Tk)) is the conditional probability that r_(Tk)=1 given d_(Tk) and c_(Tk). For simplicity, it is assumed that c_(Tk)(m_(i))≠0, i=1, 2, . . . , j. Let an estimate of the covariance V_(k) of p_(Tk)=[p_(Tk)(m₁) p_(T2)(m₂) . . . p_(Tk)(m_(j))]′ be denoted by {circumflex over (V)}_(k), which is a j×j matrix. By the weighted least squares method, if {circumflex over (V)}_(k) is invertible, an estimate {circumflex over (P)}_(Tk) of P(r_(Tk)=1|d_(Tk), c_(Tk)) is:

{circumflex over (P)} _(Tk)=(I′{circumflex over (V)} _(k) ⁻¹ I)⁻¹ I′{circumflex over (V)} _(k) ⁻¹ p _(Tk)   (62)

where I:=[1 . . . 1]′. This is an unbiased estimate of P(r_(Tk)=1|d_(Tk), c_(Tk)) with the following error variance:

s _(k) ²=(I′{circumflex over (V)} _(k) ⁻¹ I)⁻¹   (63)

If {circumflex over (V)}_(k) is not invertible, a method of treating multicolinearity can be applied. For example, {circumflex over (V)}_(k) may be replaced with {circumflex over (V)}_(k)+σI in (62) and (63) for a small σ.

The formulas, (62) and (63), are derived under the assumption that c_(Tk)(m_(i))≠0, i=1, 2, . . . , η. If c_(Tk)(m_(i))=0, the feature subvector of x_(T) with the feature subvector index m_(i) should be excluded in the determination of {circumflex over (P)}_(Tk) and s_(k) ². For simplicity, if c_(Tk)(m_(i))=0, we set p_(Tk)(m_(i))=½ and {circumflex over (V)}_(kii)={circumflex over (V)}_(kij)={circumflex over (V)}_(kji)=1000 η for j/≠i, in (62) and (63) to virtually achieve the exclusion of x_(T)(m_(i)).

A simple way to find an estimate {circumflex over (V)}_(k) is the following: Assume that the off-diagonal entries of {circumflex over (V)}_(k) to be zero, i.e., the i×j-th entry {circumflex over (V)}_(kij) of {circumflex over (V)}_(k) is equal to 0 for i≠j. Under this assumption, the weighted least squares estimate of P_(Tk) and its estimation error variance are easily determined, respectively, by

$\begin{matrix} {{\hat{P}}_{\tau \; k} = {\left( {\sum\limits_{i = 1}^{\eta}{\hat{V}}_{kii}^{- 1}} \right)^{- 1}{\sum\limits_{i = 1}^{\eta}{{\hat{V}}_{kii}^{- 1}{p_{\tau \; k}\left( m_{i} \right)}}}}} & (64) \\ {s_{k}^{2} = \left( {\sum\limits_{i = 1}^{\eta}{\hat{V}}_{kii}^{- 1}} \right)^{- 1}} & (65) \end{matrix}$

where {circumflex over (V)}_(kii)=p_(Tk)(m_(i)) (1−p_(Tk)(m_(i))), and if p_(Tk)(m_(i))=a_(Tk)(m_(i))/c_(Tk)(m_(i))>1−

or <

for some small positive number

, we set {circumflex over (V)}_(kii)=

(1−

). Here

is usually set equal to 0.05. If c_(Tk)(m_(i))=0, we set p_(Tk)(m_(i))=½ and {circumflex over (V)}_(kii)=1000 η in the above two formulas.

A pseudo-program for combining probabilities using is shown in FIG. 30.

A point estimate {circumflex over (r)}_(Tk) of r_(Tk) is obtained by setting

$\begin{matrix} {{\hat{r}}_{\tau \; k} = {{sgn}\left( {{\hat{P}}_{\tau \; k} - \frac{1}{2}} \right)}} & (66) \end{matrix}$

where sgn is the sign function defined by sgn(x)=−1 for x<0; sgn(x)=0 for x=0; and sgn(x)=1 for x>0.

CONCLUSION, RAMIFICATION, AND SCOPU OF INVENTION

Many embodiments of the present invention are disclosed, which can achieve the objectives listed in the “Summary” of this invention disclosure. While our descriptions hereinabove contain many specificities, these should not be construed as limitations on the scope of the invention, but rather as an exemplification of preferred embodiments. In addition to these embodiments, those skilled in the art will recognize that other embodiments are possible within the teachings of the present invention. Accordingly, the scope of the present invention should be limited only by the appended claims and their appropriately construed legal equivalents. 

1. A system for processing exogenous feature vectors to recognize patterns, each exogenous feature vector being a feature vector input to said system, said system comprising at least one processing unit that comprises (a) storage means for storing at least one expansion correlation matrix; (b) expansion means, responsive to a feature subvector input to said processing unit, for generating at least one orthogonal expansion of a subvector of said feature subvector; and (c) estimation means for using said at least one expansion correlation matrix and said at least one orthogonal expansion to produce a representative of a probability distribution of a label of said feature subvector.
 2. The system defined in claim 1, wherein said at least one expansion correlation matrix is an expansion correlation matrix on a rotation/translation/scaling suite of a subvector of a feature subvector index.
 3. The system defined in claim 1, wherein at least one expansion correlation matrix is a sum of expansion correlation matrices on rotation/translation/scaling suites of translations of a subvector of a feature subvector index.
 4. The system defined in claim 1, wherein said at least one processing unit further comprises a masking matrix, that, as a matrix transformation, transforms an orthogonal expansion of a subvector of a feature subvector input to said processing unit, by setting automatically selected components of said subvector equal to zero to allow said estimation means to use an orthogonal expansion of the resultant in producing a representative of a probability distribution of a label of said feature subvector.
 5. The system defined in claim 4, wherein, in said at least one processing unit, a plurality of expansion correlation matrices are submatrices of a general expansion correlation matrix, a plurality of masking matrices are submatrices of a general masking matrix, and a plurality of orthogonal expansions are subvectors of a general orthogonal expansion.
 6. The system defined in claim 1, wherein said at least one processing unit is selected from the group consisting of supervised processing units, unsupervised processing units and supervised/unsupervised processing units.
 7. The system defined in claim 1, wherein said at least one processing unit further comprises supervised learning means, responsive to a feature subvector and a label of said feature subvector provided from outside said system, for adjusting said at least one expansion correlation matrix.
 8. The system defined in claim 1, said at least one processing unit further comprises conversion means for converting a representative of a probability distribution produced by said estimation means into a ternary vector, and at least one component of said ternary vector is a component of a feature subvector that is input to another processing unit.
 9. The system defined in claim 8, wherein said conversion means generates a plurality of ternary vectors in response to an exogenous feature vector.
 10. The system defined in claim 1, wherein said at least one processing unit further comprises unsupervised learning means, responsive to a feature subvector input to said at least one processing unit, for adjusting said at least one expansion correlation matrix.
 11. The system defined in claim 8 for processing exogenous feature vectors in sequences of exogenous feature vectors, wherein a plurality of components of a ternary vector, generated by a first processing unit in processing a certain exogenous feature vector in a sequence, are included as components, after a time delay, in a feature subvector that is input to a processing unit in processing an exogenous feature vector subsequent to said certain exogenous feature vector in said sequence.
 12. The system defined in claim 1, further comprising combination means for combining a plurality of representatives of probability distributions of a common label of feature subvectors into a representative of a probability distribution of said common label.
 13. A system for processing exogenous feature vectors to recognize patterns, each exogenous feature vector being a feature vector input to said system, said system comprising a plurality of ordered layers of processing units, each processing unit comprising (a) storage means for storing at least one general expansion correlation matrix; (b) expansion means, responsive to a feature subvector input to said processing unit, for generating a general orthogonal expansion of said feature subvector; and (c) estimation means for using said at least one general expansion correlation matrix and said general orthogonal expansion to produce a representative of a probability distribution of a label of said feature subvector, and at least one of said processing units in a layer further comprising conversion means for converting a representative of a probability distribution into a ternary vector, wherein an exogenous feature vector is input to the lowest-ordered layer; and a plurality of components of a feature vector that is input to a certain layer other than said lowest-ordered layer are components of a ternary vector produced by conversion means in a processing unit in a layer that is lower-ordered than said certain layer.
 14. The system defined in claim 13, wherein, wherein said conversion means generates a plurality of ternary vectors in response to an exogenous feature vector.
 15. The system defined in claim 13, wherein at least one general expansion correlation matrix is a general expansion correlation matrix on a rotation/translation/scaling suite of a feature subvector index.
 16. The system defined in claim 13, wherein at least one general expansion correlation matrix is a sum of general expansion correlation matrices on rotation/translation/scaling suites of translations of a feature subvector index.
 17. The system defined in claim 13, wherein at least one processing unit further comprises a general masking matrix, that, as a matrix transformation, transforms a general orthogonal expansion of a feature subvector input to said processing unit, by setting automatically selected components of said feature subvector equal to zero to allow said estimation means to use a general orthogonal expansion of the resultant in producing a representative of a probability distribution of a label of said feature subvector.
 18. The system defined in claim 13, wherein at least one processing unit is selected from the group consisting of supervised processing units, unsupervised processing units and supervised/unsupervised processing units.
 19. The system defined in claim 13, wherein at least one processing unit comprises supervised learning means, responsive to a feature subvector input to said processing unit and a label of said feature subvector provided from outside said system, for adjusting said at least one general expansion correlation matrix.
 20. The system defined in claim 13, wherein at least one processing unit comprises unsupervised learning means, responsive to a feature subvector input to said processing unit, for adjusting said at least one general expansion correlation matrix.
 21. The system defined in claim 13 for processing exogenous feature vectors in sequences of exogenous feature vectors, further comprising feedback means for including a plurality of components of a ternary vector generated by a processing unit in a layer in processing a certain exogenous feature vector in a sequence, after a time delay, as components in a feature subvector that is input to a processing unit in a layer not ordered higher than said layer for processing an exogenous feature vector subsequent to said certain exogenous feature vector in said sequence.
 22. The system defined in claim 13, further comprising combination means for combining a plurality of representatives of probability distributions of a common label of feature subvectors into a representative of a probability distribution of said common label.
 23. A machine for producing an orthogonal expansion v of a ternary vector v=[v₁ v₂ . . . v_(M)]′, said machine comprising (a) multiplication means, responsive to two ternary digits, for producing a product of said two ternary digits; and (b) construction means for using said multiplication means for computing and including components of an othogonal expansion v of v.
 24. The machine defined in claim 23, wherein said construction means first sets {hacek over (v)}(1) equal to [1 v₁]′, and then uses a recursive formula, {hacek over (v)}(1, . . . , j+1)=[{hacek over (v)}′(1, . . . , j) v_(j+1){hacek over (v)}′(1, . . . , j)]′, to generate {hacek over (v)}(1, . . . , j+1) for j−1, . . . , M−1 recursively, the resultant vector {hacek over (v)}(1, . . . , M) being said orthogonal expansion {hacek over (v)}. 